Daily TMLR digest for Jul 18, 2025
TMLR
New certifications
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Survey Certification: A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems
Zixuan Ke, Fangkai Jiao, Yifei Ming, Xuan-Phi Nguyen, Austin Xu, Do Xuan Long, Minzhi Li, Chengwei Qin, PeiFeng Wang, silvio savarese, Caiming Xiong, Shafiq Joty
https://openreview.net/forum?id=SlsZZ25InC
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Featured Certification: The Geometry of Phase Transitions in Diffusion Models: Tubular Neighbourhoods and Singularities
Manato Yaguchi, Kotaro Sakamoto, Ryosuke Sakamoto, Masato Tanabe, Masatomo Akagawa, Yusuke Hayashi, Masahiro Suzuki, Yutaka Matsuo
https://openreview.net/forum?id=ahVFKFLYk2
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Accepted papers
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Title: Revisiting Data Augmentation for Ultrasound Images
Authors: Adam Tupper, Christian Gagné
Abstract: Data augmentation is a widely used and effective technique to improve the generalization performance of deep neural networks. Yet, despite often facing limited data availability when working with medical images, it is frequently underutilized. This appears to come from a gap in our collective understanding of the efficacy of different augmentation techniques across different tasks and modalities. One modality where this is especially true is ultrasound imaging. This work addresses this gap by analyzing the effectiveness of different augmentation techniques at improving model performance across a wide range of ultrasound image analysis tasks. To achieve this, we introduce a new standardized benchmark of 14 ultrasound image classification and semantic segmentation tasks from 10 different sources and covering 11 body regions. Our results demonstrate that many of the augmentations commonly used for tasks on natural images are also effective on ultrasound images, even more so than augmentations developed specifically for ultrasound images in some cases. We also show that diverse augmentation using TrivialAugment, which is widely used for natural images, is also effective for ultrasound images. Moreover, our proposed methodology represents a structured approach for assessing various data augmentations that can be applied to other contexts and modalities.
URL: https://openreview.net/forum?id=iGcxlTLIL5
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Title: A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems
Authors: Zixuan Ke, Fangkai Jiao, Yifei Ming, Xuan-Phi Nguyen, Austin Xu, Do Xuan Long, Minzhi Li, Chengwei Qin, PeiFeng Wang, silvio savarese, Caiming Xiong, Shafiq Joty
Abstract: Reasoning is a fundamental cognitive process that enables logical inference, problem-solving, and decision-making. With the rapid advancement of large language models (LLMs), reasoning has emerged as a key capability that distinguishes advanced AI systems from conventional models that empower chatbots. In this survey, we categorize existing methods along two orthogonal dimensions: (1) Regimes, which define the stage at which reasoning is achieved (either at inference time or through dedicated training); and (2) Architectures, which determine the components involved in the reasoning process, distinguishing between standalone LLMs and agentic compound systems that incorporate external tools, and multiagent collaborations. Within each dimension, we analyze two key perspectives: (1) Input level, which focuses on techniques that construct high-quality prompts that the LLM condition on; and (2) Output level, which methods that refine multiple sampled candidates to enhance reasoning quality. This categorization provides a systematic understanding of the evolving landscape of LLM reasoning, highlighting emerging trends such as the shift from inference-scaling to learning-to-reason (e.g., DeepSeek-R1), and the transition to agentic workflows (e.g., OpenAI Deep Research, Manus Agent). Additionally, we cover a broad spectrum of learning algorithms, from supervised fine-tuning to reinforcement learning such as PPO and GRPO, and the training of reasoners and verifiers. We also examine key designs of agentic workflows, from established patterns like generator-evaluator and LLM debate to recent innovations. Finally, we identify emerging trends, such as domain-specific reasoning systems, and open challenges, such as evaluation and data quality. This survey aims to provide AI researchers and practitioners with a comprehensive foundation for advancing reasoning in LLMs, paving the way for more sophisticated and reliable AI systems.
URL: https://openreview.net/forum?id=SlsZZ25InC
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Title: TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks
Authors: Prajna G. Malettira, Shubham Negi, Wachirawit Ponghiran, Kaushik Roy
Abstract: Spiking Neural Networks (SNNs) with their bio-inspired Leaky Integrate-and-Fire (LIF) neurons inherently capture temporal information. This makes them well-suited for sequential tasks like processing event-based data from Dynamic Vision Sensors (DVS) and event-based speech tasks. Harnessing the temporal capabilities of SNNs requires mitigating vanishing spikes during training, capturing spatio-temporal patterns and enhancing precise spike timing. To address these challenges, we propose _TSkips_, augmenting SNN architectures with forward and backward skip connections that incorporate explicit temporal delays. These connections capture long-term spatio-temporal dependencies and facilitate better spike flow over long sequences. The introduction of _TSkips_ creates a vast search space of possible configurations, encompassing skip positions and time delay values. To efficiently navigate this search space, this work leverages training-free Neural Architecture Search (NAS) to identify optimal network structures and corresponding delays. We demonstrate the effectiveness of our approach on four event-based datasets: DSEC-flow for optical flow estimation, DVS128 Gesture for hand gesture recognition and Spiking Heidelberg Digits (SHD) and Spiking Speech Commands (SSC) for speech recognition. Our method achieves significant improvements across these datasets: up to 18% reduction in Average Endpoint Error (AEE) on DSEC-flow, 8% increase in classification accuracy on DVS128 Gesture, and up to ~8% and ~16% higher classification accuracy on SHD and SSC, respectively.
URL: https://openreview.net/forum?id=hwz32S06G4
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Title: Adaptive Resolution Residual Networks — Generalizing Across Resolutions Easily and Efficiently
Authors: Léa Demeule, Mahtab Sandhu, Glen Berseth
Abstract: The majority of signal data captured in the real world uses numerous sensors with different resolutions. In practice, most deep learning architectures are fixed-resolution; they consider a single resolution at training and inference time. This is convenient to implement but fails to fully take advantage of the diverse signal data that exists. In contrast, other deep learning architectures are adaptive-resolution; they directly allow various resolutions to be processed at training and inference time. This provides computational adaptivity but either sacrifices robustness or compatibility with mainstream layers, which hinders their use. In this work, we introduce Adaptive Resolution Residual Networks (ARRNs) to surpass this tradeoff. We construct ARRNs from Laplacian residuals, which serve as generic adaptive-resolution adapters for fixed-resolution layers. We use smoothing filters within Laplacian residuals to linearly separate input signals over a series of resolution steps. We can thereby skip Laplacian residuals to cast high-resolution ARRNs into low-resolution ARRNs that are computationally cheaper yet numerically identical over low-resolution signals. We guarantee this result when Laplacian residuals are implemented with perfect smoothing kernels. We complement this novel component with Laplacian dropout, which randomly omits Laplacian residuals during training. This regularizes for robustness to a distribution of lower resolutions. This also regularizes for numerical errors that may occur when Laplacian residuals are implemented with approximate smoothing kernels. We provide a solid grounding for the advantageous properties of ARRNs through a theoretical analysis based on neural operators, and empirically show that ARRNs embrace the challenge posed by diverse resolutions with computational adaptivity, robustness, and compatibility with mainstream layers.
URL: https://openreview.net/forum?id=kTh5tFd1Mq
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Title: The Geometry of Phase Transitions in Diffusion Models: Tubular Neighbourhoods and Singularities
Authors: Manato Yaguchi, Kotaro Sakamoto, Ryosuke Sakamoto, Masato Tanabe, Masatomo Akagawa, Yusuke Hayashi, Masahiro Suzuki, Yutaka Matsuo
Abstract: Diffusion models undergo phase transitions during the generative process where data features suddenly emerge in the final stages.
The current study aims to elucidate this critical phenomenon from the geometrical perspective. We employ the concept of ``injectivity radius'', a quantity that characterises the structure of the data manifold. Through theoretical and empirical evidence, we demonstrate that phase transitions in the generative process of diffusion models are closely related to the injectivity radius. Our findings offer a novel perspective on phase transitions in diffusion models, with potential implications for improving performance and sampling efficiency.
URL: https://openreview.net/forum?id=ahVFKFLYk2
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Title: Identifying Macro Causal Effects in a C-DMG over ADMGs
Authors: Simon Matthieu Ferreira, Charles K. Assaad
Abstract: Causal effect identification using causal graphs is a fundamental challenge in causal inference. While extensive research has been conducted in this area, most existing methods assume the availability of fully specified directed acyclic graphs or acyclic directed mixed graphs. However, in complex domains such as medicine and epidemiology, complete causal knowledge is often unavailable, and only partial information about the system is accessible. This paper focuses on causal effect identification within partially specified causal graphs, with particular emphasis on cluster-directed mixed graphs (C-DMGs) which can represent many different acyclic directed mixed graphs (ADMGs). These graphs provide a higher-level representation of causal relationships by grouping variables into clusters, offering a more practical approach for handling complex systems. Unlike fully specified ADMGs, C-DMGs can contain cycles, which complicate their analysis and interpretation. Furthermore, their cluster-based nature introduces new challenges, as it gives rise to two distinct types of causal effects: macro causal effects and micro causal effects, each with different properties. In this work, we focus on macro causal effects, which describe the effects of entire clusters on other clusters. We establish that the do-calculus is both sound and complete for identifying these effects in C-DMGs over ADMGs when the cluster sizes are either unknown or of size greater than one. Additionally, we provide a graphical characterization of non-identifiability for macro causal effects in these graphs.
URL: https://openreview.net/forum?id=905LEugq6R
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Title: Deep Autoregressive Models as Causal Inference Engines
Authors: Daniel Jiwoong Im, Kevin Zhang, Nakul Verma, Kyunghyun Cho
Abstract: Existing causal inference (CI) models are often restricted to data with low-dimensional confounders and singleton actions. We propose an autoregressive (AR) CI framework capable of handling complex confounders and sequential actions commonly found in modern applications. Our approach accomplishes this using sequencification, which transforms data from an underlying causal diagram into a sequence of tokens. Sequencification not only accommodates training with data generated from a large class of DAGs, but also extends existing CI capabilities to estimate multiple causal quantities using a single model. We can directly compute probabilities from interventional distributions, simplifying inference and improving outcome prediction accuracy. We demonstrate that an AR model adapted for CI is efficient and effective in various complex applications such as navigating mazes, playing chess endgames, and evaluating the impact of certain keywords on paper acceptance rates, where we consider causal queries beyond standard reinforcement learning-type questions.
URL: https://openreview.net/forum?id=uuREHPf2ll
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Title: Interactive Large Language Models for Reliable Answering under Incomplete Context
Authors: Jing-Cheng Pang, Heng-Bo Fan, Pengyuan Wang, Jia-Hao Xiao, Nan Tang, Si-Hang Yang, Chengxing Jia, Ming-Kun Xie, Xiang Chen, Sheng-Jun Huang, Yang Yu
Abstract: The rise of large language models (LLMs) has revolutionized the way humans interact with artificial intelligence systems. However, their reliability in sensitive applications—such as personal consultations or clinical decision-making—remains limited. A critical shortfall lies in LLMs’ inherent lack of interactivity: these models generate responses even when essential context or domain-specific knowledge is absent, risking inaccurate or misleading outputs. A potential approach to mitigate this issue is to enable LLMs to pose clarifying questions, thereby uncovering the missing information required to provide accurate responses. However, previous methods often tend to greedily prompt LLMs to ask questions. This burdens the user to respond to potentially irrelevant questions and makes the system less flexible. In this paper, we introduce LaMSeI (Language Model with Selective Interaction) method, which enhances LLMs’ ability to judge when interaction is necessary under ambiguous or incomplete contexts. The motivation of LaMSeI is to measure the level of LLMs’ uncertainty about the user query, and interacts with user only when the uncertainty is high. Additionally, we incorporate active learning techniques to select the most informative questions from question candidates, for effectively uncovering the missing context. Our empirical studies, across various challenging question answering benchmarks, where LLMs are posed queries with incomplete context, demonstrate the effectiveness of LaMSeI. The method improves answer accuracy from 31.9% to 50.9%, outperforming other leading question-answering frameworks. Moreover, in experiments involving human participants, LaMSeI consistently generates answers superior to or comparable to baselines in more than 82% of the cases. Moreover, we verify the performance of LaMSeI on various LLMs, such as LLAMA2, LLAMA3, Vicuna and GPT-3.5, highlighting its capability to improve interactive language models.
URL: https://openreview.net/forum?id=nnlmcxYWlV
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New submissions
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Title: Epitope Generation for Peptide-based Cancer Vaccine using Goal-directed Wasserstein Generative Adversarial Network with Gradient Penalty
Abstract: We introduce a novel goal-directed Wasserstein Generative Adversarial Network with Gradient Penalty (GD-WGAN-GP) for training a generator capable of producing peptide sequences with high predicted immunogenicity and strong binding affinity to the human leukocyte antigen HLA-A*0201, thereby eliciting cytotoxic T-cell immune responses. The proposed GD-WGAN-GP incorporates a critic network to guide the generator in producing peptides with a strong binding affinity similar to those in the training set and a reward network to steer the generator toward producing sequences with high predicted immunogenicity. To avoid the generator prioritizing the objective of the critic at the expense of immunogenicity, we introduce a scaling factor to balance the influence of the reward in the loss of the generator. To reduce peptide repetition, we integrate the reward into the loss of the generator using two approaches: a switching mechanism that excludes the reward term when duplicated peptides are present in a batch, and otherwise multiplies it by a $\gamma_{max}$ parameter to control the reward's contribution, and (2) a repetition penalty from ORGAN, where each reward is divided by the number of occurrences of its corresponding peptide within the batch. Experiments on bladder cancer epitope sequences demonstrate that GD-WGAN-GP with the switching mechanism enables a tunable trade-off between the number of unique peptides and the average immunogenicity score via varying $\gamma_{max}$. Furthermore, the generator trained by the GD-WGAN-GP with the ORGAN’s repetition penalty achieves an optimal balance of uniqueness and immunogenicity. Across multiple datasets, GD-WGAN-GP outperforms existing methods by effectively reducing peptide redundancy while preserving high immunogenicity scores and strong binding affinity. The Python codes are provided at: \url{https://github.com/AnnonymousForPapers/GP-WGAN-GP_with_switch_and_ORGAN_penalty}.
URL: https://openreview.net/forum?id=Lff5AnexHJ
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Title: The inexact power augmented Lagrangian method for constrained nonconvex optimization
Abstract: This work introduces an unconventional inexact augmented Lagrangian method where the augmenting term is a Euclidean norm raised to a power between one and two. The proposed algorithm is applicable to a broad class of constrained nonconvex minimization problems that involve nonlinear equality constraints. In a first part of this work, we conduct a full complexity analysis of the method under a mild regularity condition, leveraging an accelerated first-order algorithm for solving the Hölder-smooth subproblems. Interestingly, this worst-case result indicates that using lower powers for the augmenting term leads to faster constraint satisfaction, albeit with a slower decrease of the dual residual. Notably, our analysis does not assume boundedness of the iterates. Thereafter, we present an inexact proximal point method for solving the weakly-convex and Hölder-smooth subproblems, and demonstrate that the combined scheme attains an improved rate that reduces to the best-known convergence rate whenever the augmenting term is a classical squared Euclidean norm. Different augmenting terms, involving a lower power, further improve the primal complexity at the cost of the dual complexity. Finally, numerical experiments validate the practical performance of unconventional augmenting terms.
URL: https://openreview.net/forum?id=63ANb4r7EM
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Title: Learning to Rank Features to Enhance Graph Neural Networks for Graph Classification
Abstract: A common strategy to enhance the predictive performance of graph neural networks (GNNs) for graph classification is to extend input graphs with node- and graph-level features. However, identifying the optimal feature set for a specific learning task remains a significant challenge, often requiring domain-specific expertise. To address this, we propose a general two-step method that automatically selects a compact, informative subset from a large pool of candidate features to improve classification accuracy. In the first step, a GNN is trained to estimate the importance of each feature for a given graph. In the second step, the model generates feature rankings for the training graphs, which are then aggregated into a global ranking. A top-ranked subset is selected from this global ranking and used to train a downstream graph classification GNN. Experiments on real-world and synthetic datasets show that our method outperforms various baselines, including models using all candidate features, and achieves state-of-the-art results on several benchmarks.
URL: https://openreview.net/forum?id=WmZGvWRAWb
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Title: H-FEX: A Symbolic Learning Method for Hamiltonian Systems
Abstract: Hamiltonian systems describe a broad class of dynamical systems governed by Hamiltonian functions, which encode the total energy and dictate the evolution of the system. Data-driven approaches, such as symbolic regression and neural network-based methods, provide a means to learn the governing equations of dynamical systems directly from observational data of Hamiltonian systems. However, these methods often struggle to accurately capture complex Hamiltonian functions while preserving energy conservation. To overcome this limitation, we propose the Finite Expression Method for learning Hamiltonian Systems (H-FEX), a symbolic learning method that introduces novel interaction nodes designed to capture intricate interaction terms effectively.
Our experiments, including those on highly stiff dynamical systems, demonstrate that H-FEX can recover Hamiltonian functions of complex systems that accurately capture system dynamics and preserve energy over long time horizons.
These findings highlight the potential of H-FEX as a powerful framework for discovering closed-form expressions of complex dynamical systems.
URL: https://openreview.net/forum?id=ksscGE8ySb
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Title: Offline Learning and Forgetting for Reasoning with Large Language Models
Abstract: Leveraging inference-time search in large language models has proven effective in further enhancing a trained model's capability to solve complex mathematical and reasoning problems. However, this approach significantly increases computational costs and inference time, as the model must generate and evaluate multiple candidate solutions to identify a viable reasoning path. To address this, we propose an effective approach that integrates search capabilities directly into the model by fine-tuning it on unpaired successful (learning) and failed reasoning paths (forgetting) derived from diverse search methods. A key challenge we identify is that naive fine-tuning can degrade the model’s search capability; we show this can be mitigated with a smaller learning rate. Extensive experiments on the challenging Game-of-24 and Countdown reasoning benchmarks show that, replacing CoT-generated data with search-generated data for offline fine-tuning improves success rates by around 23% over inference-time search baselines, while reducing inference time by 180$\times$. On top of this, our learning and forgetting objective consistently outperforms both supervised fine-tuning and preference-based methods.
URL: https://openreview.net/forum?id=RF6raEUATc
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Title: TicketLLM: Next-Generation Sparse and Low-bit Transformers with Supermask-based Method
Abstract: Strong Lottery Tickets are subnetworks within a randomly weighted network uncovered by a binary mask called supermask. They offer a promising approach to model compression by eliminating the need to store weights since their effective subnetwork can be regenerated from a fixed random seed and the supermask. However, extending this approach to large language models (LLMs) is non-trivial due to limited scalability and inefficient training dynamics of existing SLT methods. To address these challenges, we propose Adaptive Supermask (Ada-Sup), a scalable and efficient method for discovering high-quality multi-bit supermasks through an innovative quantization-based approach. Building on this method, we introduce TicketLLM, a low-bit and sparse Transformer-based LLM architecture powered by Ada-Sup. Experimental results show that Ada-Sup can discover high-quality supermasks with significantly reduced training costs compared to previous methods in both binary and multi-bit settings. Furthermore, TicketLLM outperforms BitNet b1.58 on a 1.3B parameter model with the same memory per connection, achieving 0.08 lower perplexity while operating at a higher sparsity level (50% vs. 33%). These results highlight the potential of supermask-based methods as a promising approach for building lightweight LLMs. Code will be made available upon acceptance.
URL: https://openreview.net/forum?id=sE69HKykQw
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Title: MemeSense: An Adaptive In-Context Framework for Social Commonsense Driven Meme Moderation
Abstract: Online memes are a powerful yet challenging medium for content moderation, often masking harmful intent behind humor, irony, or cultural symbolism. Conventional moderation systems “especially those relying on explicit text” frequently fail to recognize such subtle or implicit harm. We introduce MemeSense, an adaptive framework designed to generate socially grounded interventions for harmful memes by combining visual and textual understanding with curated, semantically aligned examples enriched with commonsense cues. This enables the model to detect nuanced complexed threats like misogyny, stereotyping, or vulgarity “even in memes lacking overt language”. Across multiple benchmark datasets, MemeSense outperforms state-of-the-art methods, achieving up to 35% higher semantic similarity
and 9% improvement in BERTScore for non-textual memes, and notable gains for text-rich memes as well. These results highlight MemeSense as a promising step toward safer, more context-aware AI systems for real-world content moderation. The code is available at: https://anonymous.4open.science/r/MemeSense/
URL: https://openreview.net/forum?id=ahRqI3NBiq
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Title: Information-Guided Diffusion Sampling for Dataset Distillation
Abstract: Dataset distillation aims to create a compact dataset that retains essential information while maintaining model performance. Diffusion models (DMs) have shown promise for this task but struggle in low images-per-class (IPC) settings, where generated samples lack diversity. In this paper, we address this issue from an information-theoretic perspective by identifying two key types of information that a distilled dataset must preserve: ($i$) \textit{prototype information} $\mathrm{I}(X;Y)$, which captures label-relevant features; and ($ii$) \textit{contextual information} $\mathrm{H}(X | Y)$, which preserves intra-class variability. Here, $(X,Y)$ represents the pair of random variables corresponding to the input data and its ground truth label, respectively. Observing that the required contextual information scales with IPC, we propose maximizing $\mathrm{I}(X;Y) + \beta \mathrm{H}(X | Y)$ during the DM sampling process, where $\beta$ is IPC-dependent. Since directly computing $\mathrm{I}(X;Y)$ and $\mathrm{H}(X | Y)$ is intractable, we develop \textit{variational estimations} to tightly lower-bound these quantities via a data-driven approach. Our approach, information-guided diffusion sampling (IGDS), seamlessly integrates with diffusion models and improves dataset distillation across all IPC settings. Experiments on Tiny ImageNet and ImageNet subsets show that IGDS significantly outperforms existing methods, particularly in low-IPC regimes. The code is available at \url{https://anonymous.4open.science/r/IGDS-4C0F/}.
URL: https://openreview.net/forum?id=LwLyfyWMpk
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Title: On the Benefits of Instance Decomposition in Video Prediction Models
Abstract: Video prediction is a crucial task for intelligent agents such as robots and autonomous vehicles, since it enables them to anticipate and act early on time-critical incidents. State-of-the-art video prediction methods typically model the dynamics of a scene jointly and implicitly, without any explicit decomposition into separate objects. This is challenging and potentially sub-optimal, as every object in a dynamic scene has their own pattern of movement, typically somewhat independent of others. In this paper, we investigate the benefit of explicitly modeling the objects in a dynamic scene separately within the context of latent-transformer video prediction models. We conduct detailed and carefully-controlled experiments on both synthetic and real-world datasets; our results show that decomposing a dynamic scene leads to higher quality predictions compared with models of a similar capacity that lack such decomposition.
URL: https://openreview.net/forum?id=lyqhffQbS7
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Title: Tractable Representation Learning with Probabilistic Circuits
Abstract: Probabilistic circuits (PCs) are powerful probabilistic models that enable exact and tractable inference, making them highly suitable for probabilistic reasoning and inference tasks. While dominant in neural networks, representation learning with PCs remains underexplored, with prior approaches relying on external neural embeddings or activation-based encodings. To address this gap, we introduce autoencoding probabilistic circuits (APCs), a novel framework leveraging the tractability of PCs to model probabilistic embeddings explicitly. APCs extend PCs by jointly modeling data and embeddings, obtaining embedding representations through tractable probabilistic inference. The PC encoder allows the framework to natively handle arbitrary missing data and is seamlessly integrated with a neural decoder in a hybrid, end-to-end trainable architecture enabled by differentiable sampling. Our empirical evaluation demonstrates that APCs outperform existing PC-based autoencoding methods in reconstruction quality, generate embeddings competitive with, and exhibit superior robustness in handling missing data compared to neural autoencoders. These results highlight APCs as a powerful and flexible representation learning method that exploits the probabilistic inference capabilities of PCs, showing promising directions for robust inference, out-of-distribution detection, and knowledge distillation.
URL: https://openreview.net/forum?id=h8D75pVKja
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Title: Unified People Tracking with Graph Neural Networks
Abstract: This work presents a unified, fully differentiable model for multi-people tracking that learns to associate detections into trajectories without relying on pre-computed tracklets. The model builds a dynamic spatiotemporal graph that aggregates spatial, contextual, and temporal information, enabling seamless information propagation across entire sequences. To improve occlusion handling, the graph can also encode scene-specific information. We also introduce a new large-scale dataset with 25 partially overlapping views, detailed scene reconstructions, and extensive occlusions. Experiments show the model achieves state-of-the-art performance on public benchmarks and the new dataset, with flexibility across diverse conditions. Both the dataset and approach will be publicly released to advance research in multi-people tracking.
URL: https://openreview.net/forum?id=rt6PFpGtv1
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Title: Learning Is a Kan Extension
Abstract: Previous work has demonstrated that efficient algorithms exist for computing Kan extensions and that some Kan extensions have interesting similarities to various machine learning algorithms. This paper closes the gap by proving that all error minimisation algorithms may be presented as a Kan extension. This result provides a foundation for future work to investigate the optimisation of machine learning algorithms through their presentation as Kan extensions. A corollary of this representation of error-minimising algorithms is a presentation of error from the perspective of lossy and lossless transformations of data.
URL: https://openreview.net/forum?id=xWKtKdeefL
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