GenAI4ELab/papercli-papers huggingface dataset:
High-dimension Prototype is a Better Incremental Object Detection Learner
Authors: Yanjie Wang; Liqun Chen; Tianming Zhao; Tao Zhang; Guodong Wang; Luxin Yan; Sheng Zhong; Jiahuan Zhou; Xu Zou
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Incremental object detection (IOD), surpassing simple classification, requires the simultaneous overcoming of catastrophic forgetting in both recognition and localization tasks, primarily due to the significantly higher feature space complexity. Integrating Knowledge Distillation (KD) would mitigate the occurrence of catastrophic forgetting. However, the challenge of knowledge shift caused by invisible previous task data hampers existing KD-based methods, leading to limited improvements in IOD performance. This paper aims to alleviate knowledge shift by enhancing the accuracy and granularity in describing complex high-dimensional feature spaces. To this end, we put forth a novel higher-dimension-prototype learning approach for KD-based IOD, enabling a more flexible, accurate, and fine-grained representation of feature distributions without the need to retain any previous task data. Existing prototype learning methods calculate feature centroids or statistical Gaussian distributions as prototypes, disregarding actual irregular distribution information or leading to inter-class feature overlap, which is not directly applicable to the more difficult task of IOD with complex feature space. To address the above issue, we propose a Gaussian Mixture Distribution-based Prototype (GMDP), which explicitly models the distribution relationships of different classes by directly measuring the likelihood of embedding from new and old models into class distribution prototypes in a higher dimension manner. Specifically, GMDP  dynamically adapts the component weights and corresponding means/variances of class distribution prototypes to represent both intra-class and inter-class variability more accurately. Progressing into a new task, GMDP constrains the distance between the distribution of new and previous task classes, minimizing overlap with existing classes and thus striking a balance between stability and adaptability. GMDP can be readily integrated into existing IOD methods to enhance performance further. Extensive experiments on the PASCAL VOC and MS-COCO show that our method consistently exceeds four baselines by a large margin and significantly outperforms other SOTA results under various settings.
HG-Adapter: Improving Pre-Trained Heterogeneous Graph Neural Networks with Dual Adapters
Authors: Yujie Mo; Runpeng Yu; Xiaofeng Zhu; Xinchao Wang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: The "pre-train, prompt-tuning'' paradigm has demonstrated impressive  performance for tuning pre-trained heterogeneous graph neural networks (HGNNs) by mitigating the gap between pre-trained models and downstream tasks. However, most prompt-tuning-based works may face at least two limitations: (i) the model may be insufficient to fit the graph structures well as they are generally ignored in the prompt-tuning stage, increasing the training error to decrease the generalization ability; and (ii) the model may suffer from the limited labeled data during the prompt-tuning stage, leading to a large generalization gap between the training error and the test error to further affect the model generalization. To alleviate the above limitations, we first derive the generalization error bound for existing prompt-tuning-based methods, and then propose a unified framework that combines two new adapters with potential labeled data extension to improve the generalization of pre-trained HGNN models. Specifically, we design dual structure-aware adapters to adaptively fit task-related homogeneous and heterogeneous structural information. We further design a label-propagated contrastive loss and two self-supervised losses to optimize dual adapters and incorporate unlabeled nodes as potential labeled data. Theoretical analysis indicates that the proposed method achieves a lower generalization error bound than existing methods, thus obtaining superior generalization ability. Comprehensive experiments demonstrate the effectiveness and generalization of the proposed method on different downstream tasks.
HyperPLR: Hypergraph Generation through Projection, Learning, and Reconstruction
Authors: Weihuang Wen; Tianshu Yu
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Hypergraphs are essential in modeling higher-order complex networks, excelling in representing group interactions within real-world contexts. This is particularly evident in collaboration networks, where they facilitate the capture of groupwise polyadic patterns, extending beyond traditional pairwise dyadic interactions. The use of hypergraph generators, or generative models, is a crucial method for promoting and validating our understanding of these structures. If such generators accurately replicate observed hypergraph patterns, it reinforces the validity of our interpretations. In this context, we introduce a novel hypergraph generative paradigm, **HyperPLR**, encompassing three phases: Projection, Learning, and Reconstruction. Initially, the hypergraph is projected onto a weighted graph. Subsequently, the model learns this graph's structure within a latent space, while simultaneously computing a distribution between the hyperedge and the projected graph. Finally, leveraging the learned model and distribution, HyperPLR generates new weighted graphs and samples cliques from them. These cliques are then used to reconstruct new hypergraphs by solving a specific clique cover problem.
We have evaluated HyperPLR on existing real-world hypergraph datasets, which consistently demonstrate superior performance and validate the effectiveness of our approach.
Continuity-Preserving  Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images
Authors: Aiqing Zhu; Yuting Pan; Qianxiao Li
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Continuous dynamical systems are cornerstones of many scientific and engineering disciplines.
While machine learning offers powerful tools to model these systems from trajectory data, challenges arise when these trajectories are captured as images, resulting in pixel-level observations that are discrete in nature.
Consequently, a naive application of a convolutional autoencoder can result in latent coordinates that are discontinuous in time.
To resolve this, we propose continuity-preserving convolutional autoencoders (CpAEs) to learn continuous latent states and their corresponding continuous latent dynamical models from discrete image frames. 
We present a mathematical formulation for learning dynamics from image frames, which illustrates issues with previous approaches and motivates our methodology based on promoting the continuity of convolution filters, thereby preserving the continuity of the latent states.
This approach enables CpAEs to produce latent states that evolve continuously with the underlying dynamics, leading to more accurate latent dynamical models.
Extensive experiments across various scenarios demonstrate the effectiveness of CpAEs.
SINGAPO: Single Image Controlled Generation of Articulated Parts in Objects
Authors: Jiayi Liu; Denys Iliash; Angel X Chang; Manolis Savva; Ali Mahdavi Amiri
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: We address the challenge of creating 3D assets for household articulated objects from a single image.
Prior work on articulated object creation either requires multi-view multi-state input, or only allows coarse control over the generation process.
These limitations hinder the scalability and practicality for articulated object modeling.
In this work, we propose a method to generate articulated objects from a single image.
Observing the object in a resting state from an arbitrary view, our method generates an articulated object that is visually consistent with the input image.
To capture the ambiguity in part shape and motion posed by a single view of the object, we design a diffusion model that learns the plausible variations of objects in terms of geometry and kinematics.
To tackle the complexity of generating structured data with attributes in multiple domains, we design a pipeline that produces articulated objects from high-level structure to geometric details in a coarse-to-fine manner, where we use a part connectivity graph and part abstraction as proxies.
Our experiments show that our method outperforms the state-of-the-art in articulated object creation by a large margin in terms of the generated object realism, resemblance to the input image, and reconstruction quality.
Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport
Authors: Zhenyi Zhang; Tiejun Li; Peijie Zhou
Venue: ICLR 2025 (ICLR 2025 Oral)

Abstract: Reconstructing dynamics using samples from sparsely time-resolved snapshots is an important problem in both natural sciences and machine learning. Here, we introduce a new deep learning approach for solving regularized unbalanced optimal transport (RUOT) and inferring continuous unbalanced stochastic dynamics from observed snapshots. Based on the RUOT form, our method models these dynamics without requiring prior knowledge of growth and death processes or additional information, allowing them to be learned directly from data.  Theoretically, we explore the connections between the RUOT and Schrödinger bridge problem and discuss the key challenges and potential solutions. The effectiveness of our method is demonstrated with a synthetic gene regulatory network, high-dimensional Gaussian Mixture Model, and single-cell RNA-seq data from blood development. Compared with other methods, our approach accurately identifies growth and transition patterns, eliminates false transitions, and constructs the Waddington developmental landscape. Our code is available at: [https://github.com/zhenyiizhang/DeepRUOT](https://github.com/zhenyiizhang/DeepRUOT).
Planning in Natural Language Improves LLM Search for Code Generation
Authors: Evan Z Wang; Federico Cassano; Catherine Wu; Yunfeng Bai; William Song; Vaskar Nath; Ziwen Han; Sean M. Hendryx; Summer Yue; Hugh Zhang
Venue: ICLR 2025 (ICLR 2025 Spotlight)

Abstract: While scaling training compute has led to remarkable improvements in large language models (LLMs), scaling inference compute only recently began to yield analogous gains. We hypothesize that a core missing component is a lack of diverse LLM outputs, leading to inefficient search due to models repeatedly sampling highly similar, yet incorrect generations. We empirically demonstrate that this lack of diversity can be mitigated by searching over candidate plans for solving a problem in natural language. Based on this insight, we propose PlanSearch, a novel search algorithm which shows strong results across HumanEval+, MBPP+, and LiveCodeBench (a contamination-free benchmark for competitive coding). PlanSearch generates a diverse set of observations about the problem and uses these observations to construct plans for solving the problem. By searching over plans in natural language rather than directly over code solutions, PlanSearch explores a significantly more diverse range of potential solutions compared to baseline search methods. Using PlanSearch on top of Claude 3.5 Sonnet achieves a pass@200 of 77.0% on LiveCodeBench, outperforming both the best pass-rate achieved without any search (pass@1 = 41.4%) and using standard repeated sampling on top of existing non-search models (pass@200 = 60.6%). Finally, we show that, across all models, search algorithms, and benchmarks analyzed, we can accurately predict performance gains from search as a function of the diversity over generated ideas.
Learning to Select Nodes in Branch and Bound with Sufficient Tree Representation
Authors: Sijia Zhang; Shuli Zeng; Shaoang Li; Feng Wu; Xiangyang Li
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Branch-and-bound methods are pivotal in solving Mixed Integer Linear Programming (MILP), where the challenge of node selection arises, necessitating the prioritization of different regions of the space for subsequent exploration. While machine learning techniques have been proposed to address this, two crucial problems concerning \textbf{(P1)} how to sufficiently extract features from the branch-and-bound tree, and \textbf{(P2)} how to assess the node quality comprehensively based on the features remain open. To tackle these challenges, we propose to tackle the node selection problem employing a novel Tripartite graph representation and Reinforcement learning with a Graph Neural Network model (TRGNN). The tripartite graph is theoretically proved to encompass sufficient information for tree representation in information theory. We learn node selection via reinforcement learning for learning delay rewards and give more comprehensive node metrics. Experiments show that TRGNN significantly improves the efficiency of solving MILPs compared to human-designed and learning-based node selection methods on both synthetic and large-scale real-world MILPs. Moreover, experiments demonstrate that TRGNN well generalizes to MILPs that are significantly larger than those seen during training.
Weakly Supervised Video Scene Graph Generation via Natural Language Supervision
Authors: Kibum Kim; Kanghoon Yoon; Yeonjun In; Jaehyeong Jeon; Jinyoung Moon; Donghyun Kim; Chanyoung Park
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Existing Video Scene Graph Generation (VidSGG) studies are trained in a fully supervised manner, which requires all frames in a video to be annotated, thereby incurring high annotation cost compared to Image Scene Graph Generation (ImgSGG). Although the annotation cost of VidSGG can be alleviated by adopting a weakly supervised approach commonly used for ImgSGG (WS-ImgSGG) that uses image captions, there are two key reasons that hinder such a naive adoption: 1) Temporality within video captions, i.e., unlike image captions, video captions include temporal markers (e.g., before, while, then, after) that indicate time-related details, and 2) Variability in action duration, i.e., unlike human actions in image captions, human actions in video captions unfold over varying duration. To address these issues, we propose a Natural Language-based Video Scene Graph Generation (NL-VSGG) framework that only utilizes the readily available video captions for training a VidSGG model. NL-VSGG consists of two key modules: Temporality-aware Caption Segmentation (TCS) module and Action Duration Variability-aware caption-frame alignment (ADV) module. Specifically, TCS segments the video captions into multiple sentences in a temporal order based on a Large Language Model (LLM), and ADV aligns each segmented sentence with appropriate frames considering the variability in action duration. Our approach leads to a significant enhancement in performance compared to simply applying the WS-ImgSGG pipeline to VidSGG on the Action Genome dataset. As a further benefit of utilizing the video captions as weak supervision, we show that the VidSGG model trained by NL-VSGG is able to predict a broader range of action classes that are not included in the training data, which makes our framework practical in reality.
GameArena: Evaluating LLM Reasoning through Live Computer Games
Authors: Lanxiang Hu; Qiyu Li; Anze Xie; Nan Jiang; Ion Stoica; Haojian Jin; Hao Zhang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Evaluating the reasoning abilities of large language models (LLMs) is challenging. Existing benchmarks often depend on static datasets, which are vulnerable to data contamination and may get saturated over time, or on binary live human feedback that conflates reasoning with other abilities. As the most prominent dynamic benchmark, Chatbot Arena evaluates open-ended questions in real-world settings, but lacks the granularity in assessing specific reasoning capabilities. We introduce GameArena, a dynamic benchmark designed to evaluate LLM reasoning capabilities through interactive gameplay with humans. GameArena consists of three games designed to test specific reasoning capabilities  (e.g., deductive and inductive reasoning), while keeping participants entertained and engaged. We analyze the gaming data retrospectively to uncover the underlying reasoning processes of LLMs and measure their fine-grained reasoning capabilities. We collect over 2000 game sessions and provide detailed assessments of various reasoning capabilities for five state-of-the-art LLMs. Our user study with 100 participants suggests that GameArena improves user engagement compared to Chatbot Arena. For the first time, GameArena enables the collection of step-by-step LLM reasoning data in the wild.
Recovering Manifold Structure Using Ollivier Ricci Curvature
Authors: Tristan Luca Saidi; Abigail Hickok; Andrew J. Blumberg
Venue: ICLR 2025 (ICLR 2025 Spotlight)

Abstract: We introduce ORC-ManL, a new algorithm to prune spurious edges from nearest neighbor graphs using a criterion based on Ollivier-Ricci curvature and estimated metric distortion. Our motivation comes from manifold learning: we show that when the data generating the nearest-neighbor graph consists of noisy samples from a low-dimensional manifold, edges that shortcut through the ambient space have more negative Ollivier-Ricci curvature than edges that lie along the data manifold. We demonstrate that our method outperforms alternative pruning methods and that it significantly improves performance on many downstream geometric data analysis tasks that use nearest neighbor graphs as input. Specifically, we evaluate on manifold learning, persistent homology, dimension estimation, and others. We also show that ORC-ManL can be used to improve clustering and manifold learning of single-cell RNA sequencing data. Finally, we provide empirical convergence experiments that support our theoretical findings.
T2V-Turbo-v2: Enhancing Video Model Post-Training through Data, Reward, and Conditional Guidance Design
Authors: Jiachen Li; Qian Long; Jian Zheng; Xiaofeng Gao; Robinson Piramuthu; Wenhu Chen; William Yang Wang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: In this paper, we focus on enhancing a diffusion-based text-to-video (T2V) model during the post-training phase by distilling a highly capable consistency model from a pretrained T2V model. Our proposed method, T2V-Turbo-v2, introduces a significant advancement by integrating various supervision signals, including high-quality training data, reward model feedback, and conditional guidance, into the consistency distillation process. Through comprehensive ablation studies, we highlight the crucial importance of tailoring datasets to specific learning objectives and the effectiveness of learning from diverse reward models for enhancing both the visual quality and text-video alignment. Additionally, we highlight the vast design space of conditional guidance strategies, which centers on designing an effective energy function to augment the teacher ODE solver. We demonstrate the potential of this approach by extracting motion guidance from the training datasets and incorporating it into the ODE solver, showcasing its effectiveness in improving the motion quality of the generated videos with the improved motion-related metrics from VBench and T2V-CompBench. Empirically, our T2V-Turbo-v2 establishes a new state-of-the-art result on VBench, **with a Total score of 85.13**, surpassing proprietary systems such as Gen-3 and Kling.
Diffusion Policy Policy Optimization
Authors: Allen Z. Ren; Justin Lidard; Lars Lien Ankile; Anthony Simeonov; Pulkit Agrawal; Anirudha Majumdar; Benjamin Burchfiel; Hongkai Dai; Max Simchowitz
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: We introduce Diffusion Policy Policy Optimization, DPPO, an algorithmic framework including best practices for fine-tuning diffusion-based policies (e.g. Diffusion Policy) in continuous control and robot learning tasks using the policy gradient (PG) method from reinforcement learning (RL). PG methods are ubiquitous in training RL policies with other policy parameterizations; nevertheless, they had been conjectured to be less efficient for diffusion-based policies. Surprisingly, we show that DPPO achieves the strongest overall performance and efficiency for fine-tuning in common benchmarks compared to other RL methods for diffusion-based policies and also compared to PG fine-tuning of other policy parameterizations. Through experimental investigation, we find that DPPO takes advantage of unique synergies between RL fine-tuning and the diffusion parameterization, leading to structured and on-manifold exploration, stable training, and strong policy robustness. We further demonstrate the strengths of DPPO in a range of realistic settings, including simulated robotic tasks with pixel observations, and via zero-shot deployment of simulation-trained policies on robot hardware in a long-horizon, multi-stage manipulation task.
Horizon Generalization in Reinforcement Learning
Authors: Vivek Myers; Catherine Ji; Benjamin Eysenbach
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: We study goal-conditioned RL through the lens of generalization, but not in the traditional sense of random augmentations and domain randomization. Rather, we aim to learn goal-directed policies that generalize with respect to the horizon: after training to reach nearby goals (which are easy to learn), these policies should succeed in reaching distant goals (which are quite challenging to learn). In the same way that invariance is closely linked with generalization is other areas of machine learning (e.g., normalization layers make a network invariant to scale, and therefore generalize to inputs of varying scales), we show that this notion of horizon generalization is closely linked with invariance to planning: a policy navigating towards a goal will select the same actions as if it were navigating to a waypoint en route to that goal. Horizon generalization and invariance to planning are appealing because of their potential reach: they imply that a policy trained to reach nearby goals would succeed at reaching goals that are arbitrarily more distant.Our theoretical analysis proves that both horizon generalization and planning invariance are possible, under some assumptions. We present new experimental results, as well as recalling results from prior work, in support of our theoretical results. Taken together, our results open the door to studying how techniques for invariance and generalization developed in other areas of machine learning might be adapted to achieve this alluring property.