,<title>) is featured with its abstract: <abstract>. Which category should (<node>,<title>) be classified as? GraphText [62]: Your goal is to perform node classification. You are given the information of each node in a xml format. Using the given information of a node, you need to classify the node to several choices: [<c0>: Rule_Learning, <c1>: Neural_Networks, <c2>: Case_Based, <c3>: Genetic_Algorithms, <c4>: Theory, <c5>: Reinforcement_Learning, <c6>: Probabilistic_Methods]. Remember, your answer should be in the form of the class label. <information> <feature> <center_node> <node> </center_node> <1st_feature_similarity_graph> <node> </1st_feature_similarity_graph> </feature> </information> GraphAdapter [19]: Here is a research paper from the computer science domain, and its title and abstract reads: <raw text>. Question: Based on the title and abstract provided , this paper is on \"___\" subject. Answer: GraphGPT [44]: Given a citation graph: <graph> where the 0th node is the target paper, with the following information: <raw text> Question: Which of the following specific aspect of diabetes research does this paper belong to: ’Rule_Learning’, ’Neural_Networks’, ’Case_Based’, ’Genetic_Algorithms’, ’Theory’, ’Reinforcement_Learning’, ’Probabilistic_Methods’. Directly give the full name of the most likely category of this paper. LLaGA [6]: Given a node-centered graph: <graph>, each node represents a paper, we need to classify the center node into 7 classes: Rule_Learning, Neural_Networks, Case_Based, Genetic_Algorithms, Theory, Reinforcement_Learning, Probabilistic_Methods, please tell me which class the center node belongs to? C Text Prompt Design C.1 LLM-as-Predictor Methods Table 7 presents the input prompts employed by each LLM-as-predictor method. To ensure a fair comparison, we report the results based on the custom prompts crafted in their respective papers. C.2 Zero-shot Inference Table 8 presents the input prompts for zero-shot inference using LLMs. We follow Chen et al. [8] to design prompts for three citation networks, i.e., Cora, Citeseer, and Pubmed, and design similar prompts for other datasets. D Discussion of Scaling Law According to [31], the neural scaling law on graphs “describes how model performance grows with the scale of training, which have two basic forms: data scaling law (how the performance changes with training dataset size) and model scaling law (how the performance changes with model size)”. Since we maintain the dataset splitting in a fair comparison benchmark, we only discuss the model 17 Table 8: Summary of input prompts for zero-shot inference using LLMs. The prompt format for citation networks follows Chen et al. [8], and similar formats are adapted for other domains. Cora: Paper: <paper_content>. Task: There are following categories: Case_Based, Genetic_Algorithms, Neural_Networks, Probabilistic_Methods, Reinforcement_Learning, Rule_Learning, Theory. Which category does this paper belong to? Output the most possible category of this paper, like ’XX’. Citeseer: Paper: <paper_content>. Task: There are following categories: Agents, ML (Machine Learning), IR (Information Retrieval), DB (Databases), HCI (Human-Computer Interaction), AI (Artificial Intelligence). Which category does this paper belong to? Output the most possible category of this paper, like ’XX’. Pubmed: Paper: <paper_content>. Task: There are following categories: Experimental, Diabetes Mellitus Type 1, Diabetes Mellitus Type 2. Which category does this paper belong to? Output the most possible category of this paper, like ’XX’. WikiCS: Computer Science article: <entity_content>. Task: There are following branches: Computational Linguistics, Databases, Operating Systems, Computer Architecture, Computer Security, Internet Protocols, Computer File Systems, Distributed Computing Architecture, Web Technology, Programming Language Topics. Which branch does this article belong to? Output the most possible branch of this article, like ’XX’. Instagram: Social media post of a user: <user_content>. Task: There are following categories of users: Normal Users, Commercial Users. Which category does the user belong to? Output the most possible category of this user, like ’XX’. Table 9: OFA and GLEM results on Cora dataset with different layers and parameter sizes of GNNs. Models Layers Metric Units per Layer 128 256 512 768 1024 OFA [29] 4-layer #Param. 592769 2168577 8269313 18302209 32267265 Acc. 66.10 69.15 72.05 71.13 72.97 5-layer #Param. 691457 2562561 9843713 21843457 38561793 Acc. 64.31 68.67 72.44 74.52 75.00 6-layer #Param. 790145 2956545 11418113 25384705 44856321 Acc. 64.17 69.54 75.10 73.45 74.42 GLEM [61] 2-layer #Param. 66966165 67562517 68755221 69947925 71140629 Acc. (GNN) 80.95 79.01 81.48 81.24 80.51 Acc. (LLM) 72.68 73.66 72.87 72.05 72.53 3-layer #Param. 67114773 68154645 71119125 75263253 80587029 Acc. (GNN) 80.66 81.24 81.29 81.78 81.43 Acc. (LLM) 71.47 72.92 71.71 73.07 73.07 4-layer #Param. 67263381 68746773 73483029 80578581 90033429 Acc. (GNN) 81.77 82.01 82.16 81.53 82.06 Acc. (LLM) 72.73 71.47 71.95 72.01 72.20 scaling law here. To be specific, we select OFA [29] and GLEM [61], which perform well across all datasets in GLBench, for our scaling law experiments. Since these models typically comprise both a GNN and an LLM, we explore scaling law separately for each component. To ensure a fair comparison, we adopt consistent experimental setups across different scales of GNNs and LLMs, such as “learning_rate” and “batch_size” (more details on parameters can be found in Appendix B.1), with the only variable being the scales of GNNs and LLMs. D.1 Different scales of GNNs We utilize fixed LLMs (i.e., SentenceBERT) and explore the scaling laws of GNNs in GraphLLM methods using GNNs with different layers and dimensions of hidden states. Detailed results for OFA and GLEM are shown in Table 9 and Figure 4, respectively. From the results, we observe that as the number of parameters increases, whether due to an increase in layers or the dimensions, there is no significant trend of improvement in model performance. 18 0M 10M 20M 30M 40M 50M # Params 64 66 68 70 72 74 Accuracy OFA 4 layer (R²=0.94) 5 layer (R²=1.00) 6 layer (R²=0.97) 60M 70M 80M 90M 100M 110M # Params 78 79 80 81 82 83 Accuracy GLEM_GNN 2-layer (R²=0.07) 3-layer (R²=0.00) 4-layer (R²=0.00) 60M 70M 80M 90M 100M 110M # Params 71.5 72.0 72.5 73.0 73.5 Accuracy GLEM_LLM 2-layer LLM (R²=0.00) 3-layer LLM (R²=0.00) 4-layer LLM (R²=0.00) Figure 4: Model scaling behaviors of OFA and GLEM with varying model depths. R2 close to 1 indicates that the model has a strong explanatory power. R2 close to 0 means that the model explains or predicts 0% of the relationship between the dependent and independent variables. Table 10: OFA and GLEM results on Cora dataset with different parameter sizes of LLMs. TinyBERT BERT-base BERT-large Sent-BERT_small Sent-BERT RoBERTa_base RoBERTa_large LLaMA2-7B LLaMA2-13B Method 4M 109M 335M 23M 66M 125M 355M 7B 13B GNN LLM GNN LLM GNN LLM GNN LLM GNN LLM GNN LLM GNN LLM GNN LLM GNN LLM OFA 56.33 65.52 65.18 78.97 75.24 56.00 58.03 78.72 80.56 GLEM 72.15 37.52 81.57 73.11 79.06 70.60 78.00 69.05 81.23 72.92 76.74 70.89 79.26 72.53 OOM OOM OOM OOM D.2 Different scales of LLMs We use fixed GNNs and explore the scaling laws of LLMs in GraphLLM methods using LLMs ranging from 4M to 13B. The results are shown in Table 10. For fairness, we introduce several versions of the same LLM-series for comparison, such as BERT-base and BERT-large. It can be observed that increasing the scale of LLMs has a less noticeable impact on model performance. Although OFA with LLaMA2-13B achieves the best results, the performance does not increase steadily with the increase in parameters, e.g., OFA with RoBERTa-large only achieves 58.3% Accuracy. In summary, it is hard to identify clear scaling laws, whether related to the scaling of GNNs or LLMs, which is consistent with the insights in GLBench. E Broader Discussion E.1 Limitations of GLBench In GLBench, we only consider the node classification task, due to the fact that most GraphLLM methods only support a single task and node classification is the task most methods focus on. With the development of Graph Foundation Models [35], tasks can be extended to edge-level and graphlevel as many methods such as OFA [29], LLaGA [6], and UniAug [45] already support multi-task pre-training and inference. In addition, the absence of non-text-attributed graphs is also a concern, as many real-world graphs lack textual information [25]. E.2 Potential Impacts Leveraging LLMs to assist graph tasks is a fast-growing and promising research field, covering a wide range of applications. We introduce this benchmark to call more researchers’ attention to this new paradigm, i.e., GraphLLM. The proposed benchmark GLBench can significantly facilitate the development of this community. Specifically, GLBench deeply and extensively explores the paradigm of combining LLMs into graph tasks, including three categories of methods: LLM-as-enhancer, LLM-as-predictor, and LLM-as-aligner. It provides a comprehensive evaluation of datasets across different domains. In the future, we will keep track of newly emerged techniques in the GraphLLM field and continuously update GLBench with more solid experimental results and detailed analyses. 19 Checklist 1. For all authors... (a) Do the main claims made in the abstract and introduction accurately reflect the paper’s contributions and scope? [Yes] (b) Did you describe the limitations of your work? [Yes] (c) Did you discuss any potential negative societal impacts of your work? [Yes] (d) Have you read the ethics review guidelines and ensured that your paper conforms to them? [Yes] 2. If you are including theoretical results... (a) Did you state the full set of assumptions of all theoretical results? [N/A] (b) Did you include complete proofs of all theoretical results? [N/A] 3. If you ran experiments (e.g. for benchmarks)... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] (b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] (c) Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)? [No] (d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] 4. If you are using existing assets (e.g., code, data, models) or curating/releasing new assets... (a) If your work uses existing assets, did you cite the creators? [Yes] (b) Did you mention the license of the assets? [Yes] (c) Did you include any new assets either in the supplemental material or as a URL? [Yes] (d) Did you discuss whether and how consent was obtained from people whose data you’re using/curating? [Yes] (e) Did you discuss whether the data you are using/curating contains personally identifiable information or offensive content? [Yes] 5. If you used crowdsourcing or conducted research with human subjects... (a) Did you include the full text of instructions given to participants and screenshots, if applicable? [N/A] (b) Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, if applicable? [N/A] (c) Did you include the estimated hourly wage paid to participants and the total amount spent on participant compensation? 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4,316 | Efficient Availability Attacks against Supervised and Contrastive Learning Simultaneously Yihan Wang, Yifan Zhu, Xiao-Shan Gao∗ Academy of Mathematics and Systems Science, Chinese Academy of Sciences University of Chinese Academy of Sciences {yihanwang, zhuyifan}@amss.ac.cn, xgao@mmrc.iss.ac.cn Abstract A... | 2024 | 2320 |
4,317 | Expressive Gaussian Human Avatars from Monocular RGB Video Hezhen Hu1 Zhiwen Fan1 Tianhao Wu2 Yihan Xi1 Seoyoung Lee1 Georgios Pavlakos1 Zhangyang Wang1 1 University of Texas at Austin 2 University of Cambridge Abstract Nuanced expressiveness, especially through detailed hand and facial expressi... | 2024 | 183 |
4,318 | Hydra: Bidirectional State Space Models Through Generalized Matrix Mixers Sukjun Hwang∗,1, Aakash Lahoti∗,1, Ratish Puduppully†,2, Tri Dao3, and Albert Gu1,4 1Machine Learning Department, Carnegie Mellon University 2IT University of Copenhagen 3Department of Computer Science, Princeton University 4Cartesia ... | 2024 | 3520 |
4,319 | XMask3D: Cross-modal Mask Reasoning for Open Vocabulary 3D Semantic Segmentation Ziyi Wang∗ Yanbo Wang∗ Xumin Yu Jie Zhou Jiwen Lu† Department of Automation, Tsinghua University, China {wziyi22, wyb23, yuxm20}@mails.tsinghua.edu.cn; {jzhou, lujiwen}@tsinghua.edu.cn Abstract Existing methodologies ... | 2024 | 2383 |
4,320 | Grounded Answers for Multi-agent Decision-making Problem through Generative World Model Zeyang Liu zeyang.liu@stu.xjtu.edu.cn Xinrui Yang xinrui.yang@stu.xjtu.edu.cn Shiguang Sun ssg2019@stu.xjtu.edu.cn Long Qian qianlongym@stu.xjtu.edu.cn Lipeng Wan wanlipeng77@xjtu.edu.cn Xingyu Chen∗ chenxi... | 2024 | 1479 |
4,321 | Constrained Diffusion with Trust Sampling William Huang Stanford University willsh@stanford.edu Yifeng Jiang Stanford University yifengj@stanford.edu Tom Van Wouwe Stanford University tvwouwe@stanford.edu C. Karen Liu Stanford University karenliu@cs.stanford.edu Abstract Diffusion models hav... | 2024 | 2976 |
4,322 | Perception of Knowledge Boundary for Large Language Models through Semi-open-ended Question Answering Zhihua Wen1†, Zhiliang Tian1∗, Zexin Jian1, Zhen Huang1, Pei Ke2, Yifu Gao1, Minlie Huang2, Dongsheng Li1∗ 1 College of Computer, National University of Defense Technology, Hunan, China 2 Tsinghua Universit... | 2024 | 2820 |
4,323 | Lookback Prophet Inequalities Ziyad Benomar ENSAE, Ecole Polytechnique, FairPlay joint team ziyad.benomar@ensae.fr Dorian Baudry Department of Statistics, University of Oxford dorian.baudry@ox.ac.uk Vianney Perchet CREST, ENSAE, Criteo AI LAB Fairplay joint team vianney.perchet@normalesup.org ... | 2024 | 1333 |
4,324 | Transfer Q⋆: Principled Decoding for LLM Alignment Souradip Chakraborty1∗ Soumya Suvra Ghosal1∗ Ming Yin2 Dinesh Manocha1 Mengdi Wang2 Amrit Singh Bedi3† Furong Huang1† 1University of Maryland-College Park; 2 Princeton University; 3 University of Central Florida Abstract Aligning foundation models... | 2024 | 3227 |
4,325 | DINTR: Tracking via Diffusion-based Interpolation Pha Nguyen1, Ngan Le1, Jackson Cothren1, Alper Yilmaz2, Khoa Luu1 1University of Arkansas 2Ohio State University 1{panguyen, thile, jcothre, khoaluu}@uark.edu 2yilmaz.15@osu.edu Figure 1: Diffusion-based processes. (a) Probabilistic diffusion process [1], wh... | 2024 | 2221 |
4,326 | MVGamba: Unify 3D Content Generation as State Space Sequence Modeling Xuanyu Yi1,5∗Zike Wu3∗ Qiuhong Shen2∗ Qingshan Xu1 Pan Zhou4 Joo-Hwee Lim 5 Shuicheng Yan6 Xinchao Wang2 Hanwang Zhang1 1Nanyang Technological University 2National University of Singapore 3University of British Columbia 4Sin... | 2024 | 243 |
4,327 | Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Sercan Ö. Arık Google Cloud AI Research {xingchenw, ruoxis, hootan, soarik}@google.com Abstract Large language models have demonstrated remarkable capabilities but their perf... | 2024 | 1855 |
4,328 | Curvature Clues: Decoding Deep Learning Privacy with Input Loss Curvature Deepak Ravikumar Efstathia Soufleri Kaushik Roy Department of Electrical and Computer Engineering Purdue University West Lafayette, IN 47907 {dravikum, esoufler, kaushik}@purdue.edu Abstract In this paper, we explore the prope... | 2024 | 631 |
4,329 | Long-Tailed Object Detection Pre-training: Dynamic Rebalancing Contrastive Learning with Dual Reconstruction Chen-Long Duan1, Yong Li1, Xiu-Shen Wei2∗, Lin Zhao1 1Nanjing University of Science and Technology 2School of Computer Science and Engineering, and Key Laboratory of New Generation Artificial Intelli... | 2024 | 1272 |
4,330 | Implicit Curriculum in Procgen Made Explicit Zhenxiong Tan∗ National University of Singapore zhenxiong@u.nus.edu Kaixin Wang∗† National University of Singapore kaixin96.wang@gmail.com Xinchao Wang‡ National University of Singapore xinchao@nus.edu.sg Abstract Procedurally generated environments suc... | 2024 | 646 |
4,331 | ALI-Agent: Assessing LLMs'Alignment with Human Values via Agent-based Evaluation Jingnan Zheng∗ National University of Singapore jingnan.zheng@u.nus.edu Han Wang∗ University of Illinois Urbana-Champaign hanw14@illinois.edu An Zhang† National University of Singapore anzhang@u.nus.edu Tai D. Nguyen ... | 2024 | 3142 |
4,332 | Why Go Full? Elevating Federated Learning Through Partial Network Updates Haolin Wang♢†, Xuefeng Liu♢♡, Jianwei Niu♢♡‡, Wenkai Guo♢†, Shaojie Tang♠ ♢State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China ♠Center fo... | 2024 | 3165 |
4,333 | TARP-VP: Towards Evaluation of Transferred Adversarial Robustness and Privacy on Label Mapping Visual Prompting Models Zhen Chen1 Yi Zhang2 Fu Wang3 Xingyu Zhao2 Xiaowei Huang1 Wenjie Ruan4∗ 1University of Liverpool 2University of Warwick 3University of Exeter 4USTC {cz97, xiaowei.huang}@liver... | 2024 | 217 |
4,334 | SHMT: Self-supervised Hierarchical Makeup Transfer via Latent Diffusion Models Zhaoyang Sun1,3∗ Shengwu Xiong1,2,5 Yaxiong Chen1 Fei Du3,4 Weihua Chen3,4 Fan Wang3,4 Yi Rong1,2† 1School of Computer Science and Artificial Intelligence, Wuhan University of Technology 2Sanya Science and Education Innov... | 2024 | 510 |
4,335 | Get Rid of Isolation: A Continuous Multi-task Spatio-Temporal Learning Framework Zhongchao Yi1, Zhengyang Zhou1,2,3,∗, Qihe Huang1, Yanjiang Chen1, Liheng Yu1, Xu Wang1,2, Yang Wang1,2,∗ 1University of Science and Technology of China (USTC), Hefei, China 2Suzhou Institute for Advanced Research, USTC, Suzhou, ... | 2024 | 4343 |
4,336 | Statistical Multicriteria Benchmarking via the GSD-Front Christoph Jansen1,∗ c.jansen@lancaster.ac.uk Georg Schollmeyer2,∗ georg.schollmeyer@stat.uni-muenchen.de Julian Rodemann2,∗ julian@stat.uni-muenchen.de Hannah Blocher2,∗ hannah.blocher@stat.uni-muenchen.de Thomas Augustin2 thomas.augustin@st... | 2024 | 3115 |
4,337 | BAM! Just Like That: Simple and Efficient Parameter Upcycling for Mixture of Experts Qizhen Zhang1† Nikolas Gritsch2,3 Dwaraknath Gnaneshwar2 Simon Guo4† David Cairuz2 Bharat Venkitesh2 Jakob Foerster1 Phil Blunsom1,2 Sebastian Ruder2 Ahmet Üstün3∗ Acyr Locatelli2∗ 1 University of Oxford 2 C... | 2024 | 1792 |
4,338 | The Impact of Initialization on LoRA Finetuning Dynamics Soufiane Hayou Simons Institute UC Berkeley hayou@berkeley.edu Nikhil Ghosh Dept of Statistics UC Berkeley nikhil_ghosh@berkeley.edu Bin Yu Dept of Statistics UC Berkeley binyu@berkeley.edu Abstract In this paper, we study the role o... | 2024 | 3715 |
4,339 | Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad Sayantan Choudhury∗ MBZUAI & Johns Hopkins University Nazarii Tupitsa MBZUAI & Innopolis University Nicolas Loizou Johns Hopkins University Samuel Horváth MBZUAI Martin Takáˇc MBZUAI Eduard Gorbunov MBZUAI Abstract ... | 2024 | 1503 |
4,340 | VLKEB: A Large Vision-Language Model Knowledge Editing Benchmark Han Huang1,2∗ Haitian Zhong2∗ Tao Yu2 Qiang Liu2† Shu Wu2 Liang Wang2 Tieniu Tan2,3 1University of Chinese Academy of Sciences (UCAS) 2New Laboratory of Pattern Recognition (NLPR), State Key Laboratory of Multimodal Artificial Intell... | 2024 | 294 |
4,341 | An Accelerated Gradient Method for Convex Smooth Simple Bilevel Optimization Jincheng Cao ECE Department UT Austin jinchengcao@utexas.edu Ruichen Jiang ECE Department UT Austin rjiang@utexas.edu Erfan Yazdandoost Hamedani SIE Department The University of Arizona erfany@arizona.edu Aryan Mokh... | 2024 | 1434 |
4,342 | EMVP: Embracing Visual Foundation Model for Visual Place Recognition with Centroid-Free Probing Qibo Qiu1,2,∗Shun Zhang3,∗ Haiming Gao3 Honghui Yang1 Haochao Ying4,† Wenxiao Wang5 Xiaofei He1 1 State Key Lab of CAD&CG, Zhejiang University 2 China Mobile (Zhejiang) Research & Innovation Institute, 3Zhe... | 2024 | 3842 |
4,343 | Adaptive Sampling for Efficient Softmax Approximation Tavor Z. Baharav† Eric and Wendy Schmidt Center Broad Institute Cambridge, MA, 02142 baharav@broadinstitute.org Ryan Kang† Department of Computer Science Stanford University Stanford, CA 94305 txryank@stanford.edu Colin Sullivan† AI Divisio... | 2024 | 3733 |
4,344 | Association Pattern-aware Fusion for Biological Entity Relationship Prediction Lingxiang Jia1 Yuchen Ying1 Zunlei Feng1,2∗Zipeng Zhong1 Shaolun Yao1 Jiacong Hu1 Mingjiang Duan1 Xingen Wang1,3 Jie Song1 Mingli Song1,2 1State Key Laboratory of Blockchain and Data Security, Zhejiang University 2Hangzhou High-Tec... | 2024 | 147 |
4,345 | Hierarchy-Agnostic Unsupervised Segmentation: Parsing Semantic Image Structure Simone Rossetti1,2 Fiora Pirri1,2 1DIAG, Sapienza University of Rome 2DeepPlants {rossetti,pirri}@diag.uniroma1.it {simone,fiora}@deepplants.com Abstract Unsupervised semantic segmentation aims to discover groupings within ... | 2024 | 3139 |
4,346 | Instance-adaptive Zero-shot Chain-of-Thought Prompting Xiaosong Yuan1,2†, Chen Shen3‡, Shaotian Yan3, Xiaofeng Zhang4 Liang Xie5, Wenxiao Wang6, Renchu Guan1,2, Ying Wang1,2∗, Jieping Ye3 1College of Computer Science and Technology, Jilin University 2Key Laboratory of Symbolic Computation and Knowledge Engine... | 2024 | 3986 |
4,347 | HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protection Yuxin Wang Sichuan University Chengdu, China wangyuxin1st@gmail.com Duanyu Feng Sichuan University Chengdu, China fengduanyuscu@stu.scu.edu.cn Yongfu Dai Sichuan University Chengdu, China wal.daishen@gmail.com Zhengy... | 2024 | 3179 |
4,348 | Fast and Memory-Efficient Video Diffusion Using Streamlined Inference Zheng Zhan1∗ Yushu Wu1∗ Yifan Gong1 Zichong Meng1 Zhenglun Kong12 Changdi Yang1 Geng Yuan3 Pu Zhao1† Wei Niu3 Yanzhi Wang1 1Northeastern University 2Harvard University 3University of Georgia a full-sized man rides a comi... | 2024 | 437 |
4,349 | AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties Xiayan Ji∗ Anton Xue∗ Eric Wong Oleg Sokolsky Insup Lee Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104 {xjiae,antonxue,exwong,sokolsky,lee}@seas.upenn.edu Abstract Anom... | 2024 | 514 |
4,350 | Data-Efficient Learning with Neural Programs Alaia Solko-Breslin, Seewon Choi, Ziyang Li, Neelay Velingker, Rajeev Alur, Mayur Naik, Eric Wong University of Pennsylvania {alaia,seewon,liby99,neelay,alur,mhnaik,exwong}@seas.upenn.edu Abstract Many computational tasks can be naturally expressed as a compositi... | 2024 | 468 |
4,351 | Learning-to-Cache: Accelerating Diffusion Transformer via Layer Caching Xinyin Ma1 Gongfan Fang1 Michael Bi Mi2 Xinchao Wang1∗ National University of Singapore1 Huawei Technologies Ltd.2 maxinyin@u.nus.edu, xinchao@nus.edu.sg 38.14% Layers 46.84% Layers (a) DiT-XL/2 (b) U-ViT-H/2 Figur... | 2024 | 4236 |
4,352 | The Importance of Online Data: Understanding Preference Fine-tuning via Coverage Yuda Song Carnegie Mellon University yudas@cs.cmu.edu Gokul Swamy Carnegie Mellon University gswamy@cs.cmu.edu Aarti Singh Carnegie Mellon University aarti@cs.cmu.edu J. Andrew Bagnell Aurora Innovation, Carnegie Me... | 2024 | 392 |
4,353 | Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning Ruoqi Zhang∗ Ziwei Luo∗ Jens Sjölund Thomas B. Schön Per Mattsson Department of Information Technology, Uppsala University {ruoqi.zhang,ziwei.luo,jens.sjolund,thomas.schon,per.mattsson}@it.uu.se Abstract Diffusi... | 2024 | 3138 |
4,354 | Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving Aniket Didolkar 1, Anirudh Goyal 1, Nan Rosemary Ke 4, Siyuan Guo 3, 5, Michal Valko 4, Timothy Lillicrap 4, Danilo Rezende 4, Yoshua Bengio 1, Michael Mozer 4, Sanjeev Arora 2 Abstract Metacognitive knowledge refers to human... | 2024 | 623 |
4,355 | Hidden in Plain Sight: Evaluating Abstract Shape Recognition in Vision-Language Models Arshia Hemmat University of Oxford Adam Davies∗ University of Illinois Urbana-Champaign Tom A. Lamb∗ University of Oxford Jianhao Yuan∗ University of Oxford Philip Torr University of Oxford Ashkan Khakzar Un... | 2024 | 2808 |
4,356 | Style Adaptation and Uncertainty Estimation for Multi-Source Blended-Target Domain Adaptation Yuwu Lu∗, Haoyu Huang, and Xue Hu School of Artificial Intelligence, South China Normal University {luyuwu2008, hyhuang99, hx1430940232}@163.com Abstract Blended-target domain adaptation (BTDA), which implicitly mi... | 2024 | 2762 |
4,357 | A Universal Growth Rate for Learning with Smooth Surrogate Losses Anqi Mao Courant Institute New York, NY 10012 aqmao@cims.nyu.edu Mehryar Mohri Google Research & CIMS New York, NY 10011 mohri@google.com Yutao Zhong Courant Institute New York, NY 10012 yutao@cims.nyu.edu Abstract This pape... | 2024 | 1318 |
4,358 | Geometry Awakening: Cross-Geometry Learning Exhibits Superiority over Individual Structures Yadong Sun1 Xiaofeng Cao1, Yu Wang1 Wei Ye2 Jingcai Guo3 Qing Guo4 1School of Artificial Intelligence, Jilin University, China 2College of Electronic and Information Engineering, Tongji University, China 3The Hong Kong... | 2024 | 3676 |
4,359 | What Makes Partial-Label Learning Algorithms Effective? Jiaqi Lv1,5, Yangfan Liu1, Shiyu Xia1, Ning Xu1,5, Miao Xu2, Gang Niu3,1, Min-Ling Zhang1,5, Masashi Sugiyama3,4, Xin Geng1,5∗ 1Southeast University 2The University of Queensland 3RIKEN Center for Advanced Intelligence Project 4The Univ... | 2024 | 2841 |
4,360 | Statistical-Computational Trade-offs for Density Estimation Anders Aamand University of Copenhagen aamand@mit.edu Alexandr Andoni Columbia University andoni@cs.columbia.edu Justin Y. Chen MIT justc@mit.edu Piotr Indyk MIT indyk@mit.edu Shyam Narayanan Citadel Securities∗ shyam.s.narayana... | 2024 | 3106 |
4,361 | RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold Amrith Setlur∗,1 Saurabh Garg1 Xinyang (Young) Geng2 Naman Garg3 Virginia Smith1 Aviral Kumar1,2 1Carnegie Mellon University 2Google DeepMind 3MultiOn Abstract Training on model-generated synthetic data is a promisi... | 2024 | 1361 |
4,362 | DeBaRA: Denoising-Based 3D Room Arrangement Generation Léopold Maillard1,2 Nicolas Sereyjol-Garros∗ Tom Durand2 Maks Ovsjanikov1 1LIX, École Polytechnique, IP Paris 2Dassault Systèmes {maillard,maks}@lix.polytechnique.fr {firsname.lastname}@3ds.com Abstract Generating realistic and diverse layouts... | 2024 | 3467 |
4,363 | UnSeg: One Universal Unlearnable Example Generator is Enough against All Image Segmentation Ye Sun1, Hao Zhang2, Tiehua Zhang3, Xingjun Ma1†, Yu-Gang Jiang1 1Shanghai Key Lab of Intell. Info. Processing, School of CS, Fudan University 2HKUST, 3Tongji University https://github.com/sunye23/UnSeg Abstract Im... | 2024 | 2513 |
4,364 | Interpretable Image Classification with Adaptive Prototype-based Vision Transformers Chiyu Ma Dartmouth College chiyu.ma.gr@dartmouth.edu Jon Donnelly Duke University jon.donnelly@duke.edu Wenjun Liu Dartmouth College wenjun.liu.gr@dartmouth.edu Soroush Vosoughi Dartmouth College soroush.vosou... | 2024 | 1312 |
4,365 | Enhancing Domain Adaptation through Prompt Gradient Alignment Hoang Phan∗ New York University hvp2011@nyu.edu Lam Tran∗ VinAI Research lamtt12@vinai.io Quyen Tran∗ VinAI Research quyentt15@vinai.io Trung Le Monash University trunglm@monash.edu Abstract Prior Unsupervised Domain Adaptation ... | 2024 | 1447 |
4,366 | AuctionNet: A Novel Benchmark for Decision-Making in Large-Scale Games Kefan Su1,2∗, Yusen Huo2, Zhilin Zhang2, Shuai Dou2, Chuan Yu2, Jian Xu2†, Zongqing Lu1†, Bo Zheng2 1School of Computer Science, Peking University 2Alibaba Group 1{sukefan,zongqing.lu}@pku.edu.cn 2 {huoyusen.huoyusen,zhangzhilin.pt,dou... | 2024 | 2993 |
4,367 | MomentumSMoE: Integrating Momentum into Sparse Mixture of Experts Rachel S.Y. Teo Department of Mathematics National University of Singapore rachel.tsy@u.nus.edu Tan M. Nguyen Department of Mathematics National University of Singapore tanmn@nus.edu.sg Abstract Sparse Mixture of Experts (SMoE) has ... | 2024 | 912 |
4,368 | The Mamba in the Llama: Distilling and Accelerating Hybrid Models Junxiong Wang1∗ Daniele Paliotta2,3∗ Avner May3 Alexander M. Rush1 Tri Dao3,4 1Cornell University 2University of Geneva 3Together AI 4Princeton University Abstract Linear RNN architectures, like Mamba, can be competitive with Tran... | 2024 | 1996 |
4,369 | Deep Correlated Prompting for Visual Recognition with Missing Modalities Lianyu Hu,Tongkai Shi, Wei Feng, Fanhua Shang, Liang Wan∗ College of Intelligence and Computing, Tianjin University {hly2021,stk,wfeng,fhshang,lwan}@tju.edu.cn https://github.com/hulianyuyy/Deep_Correlated_Prompting Abstract Large-sc... | 2024 | 2153 |
4,370 | Fully Distributed, Flexible Compositional Visual Representations via Soft Tensor Products Bethia Sun∗ Maurice Pagnucco Yang Song School of Computer Science and Engineering UNSW Sydney Abstract Since the inception of the classicalist vs. connectionist debate, it has been argued that the ability to syst... | 2024 | 4427 |
4,371 | Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand Md Musfiqur Rahman ∗ Purdue University Matt Jordan ∗ University of Texas at Austin Murat Kocaoglu Purdue University Abstract Causal inference from observational data plays critical role in many applications in tru... | 2024 | 2458 |
4,372 | AutoMix: Automatically Mixing Language Models Pranjal Aggarwal♢∗ Aman Madaan♣∗ Ankit Anand ‡ Srividya Pranavi Potharaju † Swaroop Mishra‡ Pei Zhou△ Aditya Gupta Dheeraj Rajagopal† Karthik Kappaganthu† Yiming Yang♠ Shyam Upadhyay† Manaal Faruqui† Mausam♢ ♠Carnegie Mellon University ♣xAI †... | 2024 | 4164 |
4,373 | CLIP in Mirror: Disentangling text from visual images through reflection Tiancheng Wang1 Yuguang Yang2 Linlin Yang4∗ Shaohui Lin5 Juan Zhang1,3 Guodong Guo6 Baochang Zhang1,3 1Institute of Artificial Intelligence, Beihang University, Beijing, China 2School of Electronic Information Engineering, Beih... | 2024 | 773 |
4,374 | EigenVI: score-based variational inference with orthogonal function expansions Diana Cai Flatiron Institute dcai@flatironinstitute.org Chirag Modi Flatiron Institute cmodi@flatironinstitute.org Charles C. Margossian Flatiron Institute cmargossian@flatironinstitute.org Robert M. Gower Flatiron In... | 2024 | 4218 |
4,375 | 3DET-Mamba: State Space Model for End-to-End 3D Object Detection Mingsheng Li1,∗, Jiakang Yuan1,∗, Sijin Chen1,2, Lin Zhang1, Anyu Zhu1, Xin Chen2, Tao Chen1,† 1 Fudan University 2 Tecent PCG ∗Equal contribution † Corresponding author Abstract Transformer-based architectures have been proven successfu... | 2024 | 1497 |
4,376 | Optimal Parallelization of Boosting Arthur da Cunha Department of Computer Science Aarhus University dac@cs.au.dk Mikael Møller Høgsgaard Department of Computer Science Aarhus University hogsgaard@cs.au.dk Kasper Green Larsen Department of Computer Science Aarhus University larsen@cs.au.dk Abs... | 2024 | 464 |
4,377 | OPEL: Optimal Transport Guided ProcedurE Learning Sayeed Shafayet Chowdhury, Soumyadeep Chandra, and Kaushik Roy Elmore Family School of Electrical and Computer Engineering Purdue University, West Lafayette, IN 47907, USA {chowdh23, chand133, kaushik}@purdue.edu Abstract Procedure learning refers to the t... | 2024 | 1915 |
4,378 | Optimal Design for Human Preference Elicitation Subhojyoti Mukherjee University of Wisconsin-Madison∗ smukherjee27@wisc.edu Anusha Lalitha AWS AI Labs Kousha Kalantari AWS AI Labs Aniket Deshmukh AWS AI Labs Ge Liu UIUC∗ Yifei Ma AWS AI Labs Branislav Kveton Adobe Research∗ Abstract Le... | 2024 | 2861 |
4,379 | TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks Benjamin Feuer1, Robin Tibor Schirrmeister2, Valeriia Cherepanova ∗3, Chinmay Hegde1, Frank Hutter2, Micah Goldblum1, Niv Cohen†1, Colin White†4 1 New York University, 2 University of Freiburg, 3 University of Maryland, 4 Abacus.AI Abstr... | 2024 | 2654 |
4,380 | Dissecting the Interplay of Attention Paths in a Statistical Mechanics Theory of Transformers Lorenzo Tiberi1,2 Francesca Mignacco3,4 Kazuki Irie1,2 Haim Sompolinsky1,2,5 1Center for Brain Science, Harvard University, Cambridge, MA, USA 2Kempner Institute for the Study of Natural and Artificial Intelligen... | 2024 | 2315 |
4,381 | BetterDepth: Plug-and-Play Diffusion Refiner for Zero-Shot Monocular Depth Estimation Xiang Zhang1,2, Bingxin Ke1, Hayko Riemenschneider2, Nando Metzger1, Anton Obukhov1, Markus Gross1,2, Konrad Schindler1, Christopher Schroers2 1ETH Zürich, 2DisneyResearch|Studios Abstract By training over large-scale da... | 2024 | 3451 |
4,382 | Neural decoding from stereotactic EEG: accounting for electrode variability across subjects Georgios Mentzelopoulos1, *, Evangelos Chatzipantazis1, Ashwin G. Ramayya2, Michelle J. Hedlund2, Vivek P. Buch2, Kostas Daniilidis1, 3, Konrad P. Kording1, and Flavia Vitale1, * 1University of Pennsylvania, 2Stanford Un... | 2024 | 3448 |
4,383 | SeeA∗: Efficient Exploration-Enhanced A∗Search by Selective Sampling Dengwei Zhao1, Shikui Tu1∗, Lei Xu1,2∗ 1Department of Computer Science and Engineering, Shanghai Jiao Tong University 2Guangdong Institute of Intelligence Science and Technology {zdwccc, tushikui, leixu}@sjtu.edu.cn Abstract Monte-Carlo ... | 2024 | 3309 |
4,384 | Dynamic Neural Regeneration: Enhancing Deep Learning Generalization on Small Datasets Vijaya Raghavan T Ramkumar1,, Elahe Arani1,2,∗, & Bahram Zonooz1,* 1Eindhoven University of Technology 2Wayve raghavijay95@gmail.com, e.arani@tue.nl, b.zonooz@tue.nl Abstract The efficacy of deep learning techniques is c... | 2024 | 2077 |
4,385 | Designs for Enabling Collaboration in Human-Machine Teaming via Interactive and Explainable Systems Rohan Paleja MIT Lincoln Laboratory Lexington, MA 02142 rohan.paleja@ll.mit.edu Michael Munje The University of Texas at Austin Austin, TX 78712 michaelmunje@utexas.edu Kimberlee Chestnut Chang, Ree... | 2024 | 2073 |
4,386 | SceneDiffuser: Efficient and Controllable Driving Simulation Initialization and Rollout Chiyu Max Jiang Yijing Bai∗ Andre Cornman∗ Christopher Davis∗ Xiukun Huang∗ Hong Jeon∗ Sakshum Kulshrestha∗ John Lambert∗ Shuangyu Li∗ Xuanyu Zhou∗ Carlos Fuertes Chang Yuan Mingxing Tan Yin Zhou Drago... | 2024 | 1771 |
4,387 | RectifID: Personalizing Rectified Flow with Anchored Classifier Guidance Zhicheng Sun1 , Zhenhao Yang3, Yang Jin1, Haozhe Chi1, Kun Xu2, Kun Xu2, Liwei Chen2, Hao Jiang1, Yang Song, Kun Gai2, Yadong Mu1∗ 1Peking University, 2Kuaishou Technology, 3University of Electronic Science and Technology of China {sun... | 2024 | 3075 |
4,388 | On the Surprising Effectiveness of Attention Transfer for Vision Transformers Alexander C. Li∗ Carnegie Mellon University Yuandong Tian FAIR Beidi Chen Carnegie Mellon University Deepak Pathak Carnegie Mellon University Xinlei Chen FAIR Abstract Conventional wisdom suggests that pre-training V... | 2024 | 3619 |
4,389 | When are dynamical systems learned from time series data statistically accurate? Jeongjin (Jayjay) Park School of Computational Science and Engineering Georgia Institute of Technology Atlanta, GA 30332 jpark3141@gatech.edu Nicole Tianjiao Yang Department of Mathematics Emory University Atlanta, GA 3... | 2024 | 1396 |
4,390 | Value-Based Deep Multi-Agent Reinforcement Learning with Dynamic Sparse Training Pihe Hu∗ Tsinghua University Beijing, China hupihe@gmail.com Shaolong Li∗ Central South University Changsha, China shaolongli16@gmail.com Zhuoran Li Tsinghua University Beijing, China lizr20@mails.tsinghua.edu.cn ... | 2024 | 883 |
4,391 | Enhancing Graph Transformers with Hierarchical Distance Structural Encoding Yuankai Luo1,3 Hongkang Li2 Lei Shi1∗ Xiao-Ming Wu3∗ 1Beihang University 2Rensselaer Polytechnic Institute 3The Hong Kong Polytechnic University luoyk@buaa.edu.cn xiao-ming.wu@polyu.edu.hk Abstract Graph transformers nee... | 2024 | 1821 |
4,392 | Hierarchical and Density-based Causal Clustering Kwangho Kim Korea University kwanghk@korea.ac.kr Jisu Kim Seoul National University jkim82133@snu.ac.kr Larry A. Wasserman Carnegie Mellon University larry@stat.cmu.edu Edward H. Kennedy Carnegie Mellon University edward@stat.cmu.edu Abstract ... | 2024 | 956 |
4,393 | Memory-Efficient LLM Training with Online Subspace Descent Kaizhao Liang†, Bo Liu†, Lizhang Chen†, Qiang Liu† †The University of Texas at Austin {kaizhaol,bliu,lzchen,lqiang}@utexas.edu Abstract Recently, a wide range of memory-efficient LLM training algorithms have gained substantial popularity. These me... | 2024 | 2054 |
4,394 | A Label is Worth a Thousand Images in Dataset Distillation Tian Qin Harvard University Cambridge, MA tqin@g.harvard.edu Zhiwei Deng Google DeepMind Mountain View, CA zhiweideng@google.com David Alvarez-Melis Harvard University & MSR Cambridge, MA dam@seas.harvard.edu Abstract Data quality ... | 2024 | 4194 |
4,395 | Inferring Neural Signed Distance Functions by Overfitting on Single Noisy Point Clouds through Finetuning Data-Driven based Priors Chao Chen1 Yu-Shen Liu1∗ Zhizhong Han2 1School of Software, Tsinghua University, Beijing, China 2Department of Computer Science, Wayne State University, Detroit, USA chencha... | 2024 | 1946 |
4,396 | Kraken: Inherently Parallel Transformers For Efficient Multi-Device Inference Rohan Baskar Prabhakar Princeton University rohanbp@princeton.edu Hengrui Zhang Princeton University hengrui.zhang@princeton.edu David Wentzlaff Princeton University wentzlaf@princeton.edu Abstract Large Transformer ne... | 2024 | 256 |
4,397 | Boosting Weakly-Supervised Referring Image Segmentation via Progressive Comprehension Zaiquan Yang, Yuhao Liu†, Jiaying Lin, Gerhard Hancke†, Rynson W.H. Lau† Department of Computer Science City University of Hong Kong {zaiquyang2-c, yuhliu9-c,jiayinlin5-c}@my.cityu.edu.hk {gp.hancke, Rynson.Lau}@ci... | 2024 | 2958 |
4,398 | EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models Jinhee Kim∗ KAIST Daejeon, South Korea seharanul17@kaist.ac.kr Taesung Kim∗ KAIST Daejeon, South Korea zkm1989@kaist.ac.kr Jaegul Choo KAIST Daejeon, South Korea jchoo@kaist... | 2024 | 990 |
4,399 | EnsIR: An Ensemble Algorithm for Image Restoration via Gaussian Mixture Models Shangquan Sun1,2 Wenqi Ren3,4∗ Zikun Liu5 Hyunhee Park6 Rui Wang1,2 Xiaochun Cao3 1Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China 2School of Cyber Security, University of Chinese Academy o... | 2024 | 4243 |
4,400 | A Benchmark Suite for Evaluating Neural Mutual Information Estimators on Unstructured Datasets Kyungeun Lee LG AI Research Seoul, Republic of Korea kyungeun.lee@lgresearch.ai Wonjong Rhee Seoul National University Seoul, Republic of Korea wrhee@snu.ac.kr Abstract Mutual Information (MI) is a funda... | 2024 | 1472 |
4,401 | Towards Multi-dimensional Explanation Alignment for Medical Classification Lijie Hu∗,1,2, Songning Lai∗,1,2,3, Wenshuo Chen∗,1,2, Hongru Xiao4 Hongbin Lin3, Lu Yu6, Jingfeng Zhang5,7, and Di Wang1,2 1Provable Responsible AI and Data Analytics (PRADA) Lab 2King Abdullah University of Science and Technology 3... | 2024 | 4119 |
4,402 | Neural Embeddings Rank: Aligning 3D latent dynamics with movements Chenggang Chen, Zhiyu Yang, Xiaoqin Wang Department of Biomedical Engineering, Johns Hopkins University cheng-gang.chen@jhu.edu Abstract Aligning neural dynamics with movements is a fundamental goal in neuroscience and brain-machine interf... | 2024 | 4492 |
4,403 | Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement Learning Qi Wang1,2∗Junming Yang3∗ Yunbo Wang1† Xin Jin2 Wenjun Zeng2 Xiaokang Yang1 1 MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, China 2 Ningbo Institute of Digital Twin, Eastern In... | 2024 | 3083 |
4,404 | An Image is Worth 32 Tokens for Reconstruction and Generation Qihang Yu1*, Mark Weber1,2*, Xueqing Deng1, Xiaohui Shen1, Daniel Cremers2, Liang-Chieh Chen1 1 ByteDance 2 Technical University Munich * equal contribution https://yucornetto.github.io/projects/titok.html 32 tokens TiTok (ours) 256 tokens ... | 2024 | 4096 |
4,405 | Detecting Bugs with Substantial Monetary Consequences by LLM and Rule-based Reasoning Brian Zhang University of Texas at Austin Austin, TX 78705 bz5346@utexas.edu Zhuo Zhang Purdue University West Lafayette, IN 47906 zhan3299@purdue.edu Abstract Financial transactions are increasingly being handle... | 2024 | 4258 |
4,406 | Policy Aggregation Parand A. Alamdari∗ University of Toronto & Vector Institute parand@cs.toronto.edu Soroush Ebadian∗ University of Toronto soroush@cs.toronto.edu Ariel D. Procaccia Harvard University arielpro@seas.harvard.edu Abstract We consider the challenge of AI value alignment with multiple... | 2024 | 2182 |
4,407 | ProSST: Protein Language Modeling with Quantized Structure and Disentangled Attention Mingchen Li2,3,5∗ Yang Tan2,3,5,∗ Xinzhu Ma2,4 Bozitao Zhong1,4 Huiqun Yu3 Ziyi Zhou1 Wanli Ouyang2,4 Bingxin Zhou1 Pan Tan1,2 Liang Hong1,2,5 1 Shanghai Jiao Tong University, China {zy-zhou,bingxin.zhou,hong... | 2024 | 1126 |
4,408 | On the Noise Robustness of In-Context Learning for Text Generation Hongfu Gao1,2∗, Feipeng Zhang2, Wenyu Jiang1,3, Jun Shu4, Feng Zheng5, Hongxin Wei1† 1Department of Statistics and Data Science, Southern University of Science and Technology 2School of Economics and Finance, Xi’an Jiaotong University 3Nationa... | 2024 | 528 |
4,409 | Bias Amplification in Language Model Evolution: An Iterated Learning Perspective Yi Ren UBC renyi.joshua@gmail.com Shangmin Guo University of Edinburgh s.guo@ed.ac.uk Linlu Qiu MIT linluqiu@mit.edu Bailin Wang MIT bailin.wang28@gmail.com Danica J. Sutherland UBC & Amii dsuth@cs.ubc.ca ... | 2024 | 1220 |
4,410 | Multi-Stage Predict+Optimize for (Mixed Integer) Linear Programs Xinyi Hu1, Jasper C.H. Lee2, Jimmy H.M. Lee1, Peter J. Stuckey3 1Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong 2Department of Computer Science, University of California, Davis, USA ... | 2024 | 2068 |
4,411 | Understanding the Differences in Foundation Models: Attention, State Space Models, and Recurrent Neural Networks Jerome Sieber∗ ETH Zurich Zurich, Switzerland jsieber@ethz.ch Carmen Amo Alonso∗ ETH Zurich Zurich, Switzerland camoalonso@ethz.ch Alexandre Didier ETH Zurich Zurich, Switzerland ... | 2024 | 4275 |
4,412 | RestoreAgent: Autonomous Image Restoration Agent via Multimodal Large Language Models Haoyu Chen1, Wenbo Li2, Jinjin Gu3, Jingjing Ren1, Sixiang Chen1, Tian Ye1, Renjing Pei2, Kaiwen Zhou2, Fenglong Song2, Lei Zhu1,4∗ 1The Hong Kong University of Science and Technology (Guangzhou) 2Huawei Noah... | 2024 | 3512 |
4,413 | Small coresets via negative dependence: DPPs, linear statistics, and concentration Rémi Bardenet ∗ Univ. Lille, CNRS, Centrale Lille, UMR 9189 – CRIStAL, F-59000 Lille, France remi.bardenet@cnrs.fr Subhroshekhar Ghosh ∗ Department of Mathematics National University of Singapore 10 Lower Kent Ridge R... | 2024 | 2679 |
4,414 | Rethinking Exploration in Reinforcement Learning with Effective Metric-Based Exploration Bonus Yiming Wang1, Kaiyan Zhao1,2, Furui Liu3, Leong Hou U1∗ 1State Key Laboratory of Internet of Things for Smart City, University of Macau, Macao SAR, China 2School of Computer Science, Wuhan University, Wuhan, Chi... | 2024 | 1842 |
4,415 | GLBench: A Comprehensive Benchmark for Graph with Large Language Models Yuhan Li1∗, Peisong Wang2∗, Xiao Zhu1, Aochuan Chen1, Haiyun Jiang3†, Deng Cai4, Victor Wai Kin Chan2, Jia Li1† 1Hong Kong University of Science and Technology (Guangzhou) 2Tsinghua University 3 School of Electronic Information and Electric... | 2024 | 1340 |
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