pwc-archive/papers-with-abstracts huggingface dataset:
Enhancing Scene Classification in Cloudy Image Scenarios: A Collaborative Transfer Method with Information Regulation Mechanism using Optical Cloud-Covered and SAR Remote Sensing Images
Authors: Yuze Wang, Rong Xiao, Haifeng Li, Mariana Belgiu, Chao Tao
Date: 2025-01-08 00:00:00
Tasks: Cloud Removal, Knowledge Distillation, Scene Classification, Transfer Learning

In remote sensing scene classification, leveraging the transfer methods with well-trained optical models is an efficient way to overcome label scarcity. However, cloud contamination leads to optical information loss and significant impacts on feature distribution, challenging the reliability and stability of transferred target models. Common solutions include cloud removal for optical data or directly using Synthetic aperture radar (SAR) data in the target domain. However, cloud removal requires substantial auxiliary data for support and pre-training, while directly using SAR disregards the unobstructed portions of optical data. This study presents a scene classification transfer method that synergistically combines multi-modality data, which aims to transfer the source domain model trained on cloudfree optical data to the target domain that includes both cloudy optical and SAR data at low cost. Specifically, the framework incorporates two parts: (1) the collaborative transfer strategy, based on knowledge distillation, enables the efficient prior knowledge transfer across heterogeneous data; (2) the information regulation mechanism (IRM) is proposed to address the modality imbalance issue during transfer. It employs auxiliary models to measure the contribution discrepancy of each modality, and automatically balances the information utilization of modalities during the target model learning process at the sample-level. The transfer experiments were conducted on simulated and real cloud datasets, demonstrating the superior performance of the proposed method compared to other solutions in cloud-covered scenarios. We also verified the importance and limitations of IRM, and further discussed and visualized the modality imbalance problem during the model transfer. Codes are available at https://github.com/wangyuze-csu/ESCCS
Scaling Large Language Model Training on Frontier with Low-Bandwidth Partitioning
Authors: Lang Xu, Quentin Anthony, Jacob Hatef, Aamir Shafi, Hari Subramoni, Dhabaleswar K., Panda
Date: 2025-01-08 00:00:00
Tasks: GPU, Language Modeling, Language Modelling, Large Language Model

Scaling up Large Language Model(LLM) training involves fitting a tremendous amount of training parameters across a limited number of workers. However, methods like ZeRO-3 that drastically reduce GPU memory pressure often incur heavy communication to ensure global synchronization and consistency. Established efforts such as ZeRO++ use secondary partitions to avoid inter-node communications, given that intra-node GPU-GPU transfer generally has more bandwidth and lower latency than inter-node connections. However, as more capable infrastructure like Frontier, equipped with AMD GPUs, emerged with impressive computing capability, there is a need for investigations on the hardware topology and to develop targeted strategies to improve training efficiency. In this work, we propose a collection of communication and optimization strategies for ZeRO++ to reduce communication costs and improve memory utilization. In this paper, we propose a 3-level hierarchical partitioning specifically for the current 2nd ranked supercomputing cluster, Frontier, which aims at leveraging various bandwidths across layers of communications (GCD-GCD, GPU-GPU, and inter-node) to reduce communication overhead. For a 20B GPT model, we observe a 1.71x increase in TFLOPS per GPU when compared with ZeRO++ up to 384 GCDs and a scaling efficiency of 0.94 for up to 384 GCDs.
KN-LIO: Geometric Kinematics and Neural Field Coupled LiDAR-Inertial Odometry
Authors: Zhong Wang, Lele Ren, Yue Wen, Hesheng Wang
Date: 2025-01-08 00:00:00
Tasks: Pose Estimation, State Estimation

Recent advancements in LiDAR-Inertial Odometry (LIO) have boosted a large amount of applications. However, traditional LIO systems tend to focus more on localization rather than mapping, with maps consisting mostly of sparse geometric elements, which is not ideal for downstream tasks. Recent emerging neural field technology has great potential in dense mapping, but pure LiDAR mapping is difficult to work on high-dynamic vehicles. To mitigate this challenge, we present a new solution that tightly couples geometric kinematics with neural fields to enhance simultaneous state estimation and dense mapping capabilities. We propose both semi-coupled and tightly coupled Kinematic-Neural LIO (KN-LIO) systems that leverage online SDF decoding and iterated error-state Kalman filtering to fuse laser and inertial data. Our KN-LIO minimizes information loss and improves accuracy in state estimation, while also accommodating asynchronous multi-LiDAR inputs. Evaluations on diverse high-dynamic datasets demonstrate that our KN-LIO achieves performance on par with or superior to existing state-of-the-art solutions in pose estimation and offers improved dense mapping accuracy over pure LiDAR-based methods. The relevant code and datasets will be made available at https://**.
Integrated Offline and Online Learning to Solve a Large Class of Scheduling Problems
Authors: Anbang Liu, Zhi-Long Chen, Jinyang Jiang, Xi Chen
Date: 2025-01-08 00:00:00
Tasks: Scheduling

In this paper, we develop a unified machine learning (ML) approach to predict high-quality solutions for single-machine scheduling problems with a non-decreasing min-sum objective function with or without release times. Our ML approach is novel in three major aspects. First, our approach is developed for the entire class of the aforementioned problems. To achieve this, we exploit the fact that the entire class of the problems considered can be formulated as a time-indexed formulation in a unified manner. We develop a deep neural network (DNN) which uses the cost parameters in the time-indexed formulation as the inputs to effectively predict a continuous solution to this formulation, based on which a feasible discrete solution is easily constructed. The second novel aspect of our approach lies in how the DNN model is trained. In view of the NP-hard nature of the problems, labels (i.e., optimal solutions) are hard to generate for training. To overcome this difficulty, we generate and utilize a set of special instances, for which optimal solutions can be found with little computational effort, to train the ML model offline. The third novel idea we employ in our approach is that we develop an online single-instance learning approach to fine tune the parameters in the DNN for a given online instance, with the goal of generating an improved solution for the given instance. To this end, we develop a feasibility surrogate that approximates the objective value of a given instance as a continuous function of the outputs of the DNN, which then enables us to derive gradients and update the learnable parameters in the DNN. Numerical results show that our approach can efficiently generate high-quality solutions for a variety of single-machine scheduling min-sum problems with up to 1000 jobs.
Constraints as Rewards: Reinforcement Learning for Robots without Reward Functions
Authors: Yu Ishihara, Noriaki Takasugi, Kotaro Kawakami, Masaya Kinoshita, Kazumi Aoyama
Date: 2025-01-08 00:00:00
Tasks: Motion Generation, reinforcement-learning, Reinforcement Learning

Reinforcement learning has become an essential algorithm for generating complex robotic behaviors. However, to learn such behaviors, it is necessary to design a reward function that describes the task, which often consists of multiple objectives that needs to be balanced. This tuning process is known as reward engineering and typically involves extensive trial-and-error. In this paper, to avoid this trial-and-error process, we propose the concept of Constraints as Rewards (CaR). CaR formulates the task objective using multiple constraint functions instead of a reward function and solves a reinforcement learning problem with constraints using the Lagrangian-method. By adopting this approach, different objectives are automatically balanced, because Lagrange multipliers serves as the weights among the objectives. In addition, we will demonstrate that constraints, expressed as inequalities, provide an intuitive interpretation of the optimization target designed for the task. We apply the proposed method to the standing-up motion generation task of a six-wheeled-telescopic-legged robot and demonstrate that the proposed method successfully acquires the target behavior, even though it is challenging to learn with manually designed reward functions.
Agent Laboratory: Using LLM Agents as Research Assistants
Authors: Samuel Schmidgall, Yusheng Su, Ze Wang, Ximeng Sun, Jialian Wu, Xiaodong Yu, Jiang Liu, Michael Moor, Zicheng Liu, Emad Barsoum
Date: 2025-01-08 00:00:00
Tasks: scientific discovery

Historically, scientific discovery has been a lengthy and costly process, demanding substantial time and resources from initial conception to final results. To accelerate scientific discovery, reduce research costs, and improve research quality, we introduce Agent Laboratory, an autonomous LLM-based framework capable of completing the entire research process. This framework accepts a human-provided research idea and progresses through three stages--literature review, experimentation, and report writing to produce comprehensive research outputs, including a code repository and a research report, while enabling users to provide feedback and guidance at each stage. We deploy Agent Laboratory with various state-of-the-art LLMs and invite multiple researchers to assess its quality by participating in a survey, providing human feedback to guide the research process, and then evaluate the final paper. We found that: (1) Agent Laboratory driven by o1-preview generates the best research outcomes; (2) The generated machine learning code is able to achieve state-of-the-art performance compared to existing methods; (3) Human involvement, providing feedback at each stage, significantly improves the overall quality of research; (4) Agent Laboratory significantly reduces research expenses, achieving an 84% decrease compared to previous autonomous research methods. We hope Agent Laboratory enables researchers to allocate more effort toward creative ideation rather than low-level coding and writing, ultimately accelerating scientific discovery.
Continual Self-supervised Learning Considering Medical Domain Knowledge in Chest CT Images
Authors: Ren Tasai, Guang Li, Ren Togo, Minghui Tang, Takaaki Yoshimura, Hiroyuki Sugimori, Kenji Hirata, Takahiro Ogawa, Kohsuke Kudo, Miki Haseyama
Date: 2025-01-08 00:00:00
Tasks: Continual Self-Supervised Learning, Diversity, Self-Supervised Learning

We propose a novel continual self-supervised learning method (CSSL) considering medical domain knowledge in chest CT images. Our approach addresses the challenge of sequential learning by effectively capturing the relationship between previously learned knowledge and new information at different stages. By incorporating an enhanced DER into CSSL and maintaining both diversity and representativeness within the rehearsal buffer of DER, the risk of data interference during pretraining is reduced, enabling the model to learn more richer and robust feature representations. In addition, we incorporate a mixup strategy and feature distillation to further enhance the model's ability to learn meaningful representations. We validate our method using chest CT images obtained under two different imaging conditions, demonstrating superior performance compared to state-of-the-art methods.
UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles
Authors: Abhishek Balasubramaniam, Febin P Sunny, Sudeep Pasricha
Date: 2025-01-08 00:00:00
Tasks: 3D Object Detection, Autonomous Vehicles, Model Compression, Object, object-detection, Object Detection, Quantization

To enhance perception in autonomous vehicles (AVs), recent efforts are concentrating on 3D object detectors, which deliver more comprehensive predictions than traditional 2D object detectors, at the cost of increased memory footprint and computational resource usage. We present a novel framework called UPAQ, which leverages semi-structured pattern pruning and quantization to improve the efficiency of LiDAR point-cloud and camera-based 3D object detectors on resource-constrained embedded AV platforms. Experimental results on the Jetson Orin Nano embedded platform indicate that UPAQ achieves up to 5.62x and 5.13x model compression rates, up to 1.97x and 1.86x boost in inference speed, and up to 2.07x and 1.87x reduction in energy consumption compared to state-of-the-art model compression frameworks, on the Pointpillar and SMOKE models respectively.
CURing Large Models: Compression via CUR Decomposition
Authors: SangHyeon Park, Soo-Mook Moon
Date: 2025-01-08 00:00:00
Tasks: Model Compression

Large deep learning models have achieved remarkable success but are resource-intensive, posing challenges such as memory usage. We introduce CURing, a novel model compression method based on CUR matrix decomposition, which approximates weight matrices as the product of selected columns (C) and rows (R), and a small linking matrix (U). We apply this decomposition to weights chosen based on the combined influence of their magnitudes and activations. By identifying and retaining informative rows and columns, CURing significantly reduces model size with minimal performance loss. For example, it reduces Llama3.1-8B's parameters to 7.32B (-9%) in just 129 seconds, over 20 times faster than prior compression methods.
Generative Dataset Distillation Based on Self-knowledge Distillation
Authors: Longzhen Li, Guang Li, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama
Date: 2025-01-08 00:00:00
Tasks: Dataset Distillation, Knowledge Distillation, Self-Knowledge Distillation

Dataset distillation is an effective technique for reducing the cost and complexity of model training while maintaining performance by compressing large datasets into smaller, more efficient versions. In this paper, we present a novel generative dataset distillation method that can improve the accuracy of aligning prediction logits. Our approach integrates self-knowledge distillation to achieve more precise distribution matching between the synthetic and original data, thereby capturing the overall structure and relationships within the data. To further improve the accuracy of alignment, we introduce a standardization step on the logits before performing distribution matching, ensuring consistency in the range of logits. Through extensive experiments, we demonstrate that our method outperforms existing state-of-the-art methods, resulting in superior distillation performance.
GNN-based Decentralized Perception in Multirobot Systems for Predicting Worker Actions
Authors: Ali Imran, Giovanni Beltrame, David St-Onge
Date: 2025-01-08 00:00:00
Tasks: Decision Making

In industrial environments, predicting human actions is essential for ensuring safe and effective collaboration between humans and robots. This paper introduces a perception framework that enables mobile robots to understand and share information about human actions in a decentralized way. The framework first allows each robot to build a spatial graph representing its surroundings, which it then shares with other robots. This shared spatial data is combined with temporal information to track human behavior over time. A swarm-inspired decision-making process is used to ensure all robots agree on a unified interpretation of the human's actions. Results show that adding more robots and incorporating longer time sequences improve prediction accuracy. Additionally, the consensus mechanism increases system resilience, making the multi-robot setup more reliable in dynamic industrial settings.
Fixed Points of Deep Neural Networks: Emergence, Stability, and Applications
Authors: L. Berlyand, V. Slavin
Date: 2025-01-07 00:00:00

We present numerical and analytical results on the formation and stability of a family of fixed points of deep neural networks (DNNs). Such fixed points appear in a class of DNNs when dimensions of input and output vectors are the same. We demonstrate examples of applications of such networks in supervised, semi-supervised and unsupervised learning such as encoding/decoding of images, restoration of damaged images among others. We present several numerical and analytical results. First, we show that for untrained DNN's with weights and biases initialized by normally distributed random variables the only one fixed point exists. This result holds for DNN with any depth (number of layers) $L$, any layer width $N$, and sigmoid-type activation functions. Second, it has been shown that for a DNN whose parameters (weights and biases) are initialized by ``light-tailed'' distribution of weights (e.g. normal distribution), after training the distribution of these parameters become ``heavy-tailed''. This motivates our study of DNNs with ``heavy-tailed'' initialization. For such DNNs we show numerically %existence and stability that training leads to emergence of $Q(N,L)$ fixed points, where $Q(N,L)$ is a positive integer which depends on the number of layers $L$ and layer width $N$. We further observe numerically that for fixed $N = N_0$ the function $Q(N_0, L)$ is non-monotone, that is it initially grows as $L$ increases and then decreases to 1. This non-monotone behavior of $Q(N_0, L)$ is also obtained by analytical derivation of equation for Empirical Spectral Distribution (ESD) of input-output Jacobian followed by numerical solution of this equation.
HIVEX: A High-Impact Environment Suite for Multi-Agent Research (extended version)
Authors: Philipp Dominic Siedler
Date: 2025-01-07 00:00:00

Games have been vital test beds for the rapid development of Agent-based research. Remarkable progress has been achieved in the past, but it is unclear if the findings equip for real-world problems. While pressure grows, some of the most critical ecological challenges can find mitigation and prevention solutions through technology and its applications. Most real-world domains include multi-agent scenarios and require machine-machine and human-machine collaboration. Open-source environments have not advanced and are often toy scenarios, too abstract or not suitable for multi-agent research. By mimicking real-world problems and increasing the complexity of environments, we hope to advance state-of-the-art multi-agent research and inspire researchers to work on immediate real-world problems. Here, we present HIVEX, an environment suite to benchmark multi-agent research focusing on ecological challenges. HIVEX includes the following environments: Wind Farm Control, Wildfire Resource Management, Drone-Based Reforestation, Ocean Plastic Collection, and Aerial Wildfire Suppression. We provide environments, training examples, and baselines for the main and sub-tasks. All trained models resulting from the experiments of this work are hosted on Hugging Face. We also provide a leaderboard on Hugging Face and encourage the community to submit models trained on our environment suite.
Multimodal Multihop Source Retrieval for Web Question Answering
Authors: Navya Yarrabelly, Saloni Mittal
Date: 2025-01-07 00:00:00
Tasks: Multi-hop Question Answering, Question Answering, Retrieval

This work deals with the challenge of learning and reasoning over multi-modal multi-hop question answering (QA). We propose a graph reasoning network based on the semantic structure of the sentences to learn multi-source reasoning paths and find the supporting facts across both image and text modalities for answering the question. In this paper, we investigate the importance of graph structure for multi-modal multi-hop question answering. Our analysis is centered on WebQA. We construct a strong baseline model, that finds relevant sources using a pairwise classification task. We establish that, with the proper use of feature representations from pre-trained models, graph structure helps in improving multi-modal multi-hop question answering. We point out that both graph structure and adjacency matrix are task-related prior knowledge, and graph structure can be leveraged to improve the retrieval performance for the task. Experiments and visualized analysis demonstrate that message propagation over graph networks or the entire graph structure can replace massive multimodal transformers with token-wise cross-attention. We demonstrated the applicability of our method and show a performance gain of \textbf{4.6$\%$} retrieval F1score over the transformer baselines, despite being a very light model. We further demonstrated the applicability of our model to a large scale retrieval setting.
Learning to Transfer Human Hand Skills for Robot Manipulations
Authors: Sungjae Park, Seungho Lee, Mingi Choi, Jiye Lee, Jeonghwan Kim, Jisoo Kim, Hanbyul Joo
Date: 2025-01-07 00:00:00
Tasks: Object, Robot Manipulation

We present a method for teaching dexterous manipulation tasks to robots from human hand motion demonstrations. Unlike existing approaches that solely rely on kinematics information without taking into account the plausibility of robot and object interaction, our method directly infers plausible robot manipulation actions from human motion demonstrations. To address the embodiment gap between the human hand and the robot system, our approach learns a joint motion manifold that maps human hand movements, robot hand actions, and object movements in 3D, enabling us to infer one motion component from others. Our key idea is the generation of pseudo-supervision triplets, which pair human, object, and robot motion trajectories synthetically. Through real-world experiments with robot hand manipulation, we demonstrate that our data-driven retargeting method significantly outperforms conventional retargeting techniques, effectively bridging the embodiment gap between human and robotic hands. Website at https://rureadyo.github.io/MocapRobot/.
Reasoning-Enhanced Self-Training for Long-Form Personalized Text Generation
Authors: Alireza Salemi, Cheng Li, Mingyang Zhang, Qiaozhu Mei, Weize Kong, Tao Chen, Zhuowan Li, Michael Bendersky, Hamed Zamani
Date: 2025-01-07 00:00:00
Tasks: Form, Response Generation, Text Generation

Personalized text generation requires a unique ability of large language models (LLMs) to learn from context that they often do not encounter during their standard training. One way to encourage LLMs to better use personalized context for generating outputs that better align with the user's expectations is to instruct them to reason over the user's past preferences, background knowledge, or writing style. To achieve this, we propose Reasoning-Enhanced Self-Training for Personalized Text Generation (REST-PG), a framework that trains LLMs to reason over personal data during response generation. REST-PG first generates reasoning paths to train the LLM's reasoning abilities and then employs Expectation-Maximization Reinforced Self-Training to iteratively train the LLM based on its own high-reward outputs. We evaluate REST-PG on the LongLaMP benchmark, consisting of four diverse personalized long-form text generation tasks. Our experiments demonstrate that REST-PG achieves significant improvements over state-of-the-art baselines, with an average relative performance gain of 14.5% on the benchmark.