Introduces 'Curse of Rarity' concept, analyzing how low frequency of safety-critical events in high-dimensional driving environments hampers autonomous vehicle safety.

Henry X. Liu, Shuo Feng

Deep learning-based 6DoF grasp synthesis using sampling, regression, RL, and exemplars, greatly improving robotic grasp success rates.

Rhys Newbury, Morris Gu, Lachlan Chumbley et al.

This paper introduces TokenGT, a pure Transformer for graphs using node and edge tokens, theoretically matching or surpassing 2-IGN in expressive power.

Jinwoo Kim, Tien Dat Nguyen, Seonwoo Min et al.

Dual decision framework combining known class discriminator and class-agnostic object head improves open-set panoptic segmentation PQ by over 30%.

Hai-Ming Xu, Hao Chen, Lingqiao Liu et al.

WebShop employs RL and imitation learning on a dataset of 1.18 million products, achieving 29% success in complex web tasks, surpassing rule-based methods (9.6%).

Shunyu Yao, Howard Chen, John Yang et al.

Proposes CAGCN, a recommendation-oriented GNN leveraging CIR to enhance collaboration signals, surpassing 1-WL discriminative power.

Yu Wang, Yuying Zhao, Yi Zhang et al.

Proposes an interpretable predictor framework using variational autoencoders and MCMC to select high-information queries, enhancing transparency.

Aditya Chattopadhyay, Stewart Slocum, Benjamin D. Haeffele et al.

Proposes QEDB, a question-answer pair-based knowledge base, combining QA generation and entity linking to enhance complex query answering.

Wenhu Chen, William W. Cohen, Michiel De Jong et al.

Introduces Autocast dataset with news retrieval, using large models (T5, GPT-2) for future event forecasting; accuracy 65% vs. 92% human baseline.

Andy Zou, Tristan Xiao, Ryan Jia et al.

ROT algorithm accelerates imitation learning with regularized optimal transport, achieving 7.8x faster to 90% expert performance.

Siddhant Haldar, Vaibhav Mathur, Denis Yarats et al.

Neural radiance fields (NeRF) combined with depth-guided ray marching enable high-fidelity 3D reconstruction of deformable tissues from single-view stereo videos, outperforming state-of-the-art methods.

Yuehao Wang, Yonghao Long, Siu Hin Fan et al.

This paper proves that neural networks trained via gradient descent can efficiently learn high-degree polynomials depending on few features, with sample complexity much lower than kernel methods.

Alex Damian, Jason D. Lee, Mahdi Soltanolkotabi

Proposes data pruning to surpass power-law neural scaling, achieving exponential error reduction; validated on ResNet with CIFAR-10, SVHN, ImageNet.

Ben Sorscher, Robert Geirhos, Shashank Shekhar et al.

Proposes an atom-centered density prediction model using sparse kernel methods and gradient optimization, achieving 0.1 meV/atom energy accuracy in liquid water.

Andrea Grisafi, Alan M. Lewis, Mariana Rossi et al.

Proposed Structural Entropy Guided Pooling (SEP), avoiding local structure damage and improving graph and node classification accuracy.

Junran Wu, Xueyuan Chen, Ke Xu et al.

Proposes HO-GCN with object dynamic descriptors for predicting human-large object interactions, achieving state-of-the-art accuracy and generalization.

Weilin Wan, Lei Yang, Lingjie Liu et al.

VPT uses a small labeled dataset to train an inverse dynamics model, then leverages large-scale unlabeled videos for pretraining, achieving near-human performance in Minecraft.

Bowen Baker, Ilge Akkaya, Peter Zhokhov et al.

Proposes Behavior Transformer (BeT), combining action discretization and multi-task residual correction, to model multi-modal behaviors with high accuracy.

Nur Muhammad Mahi Shafiullah, Zichen Jeff Cui, Ariuntuya Altanzaya et al.

This study uses a synthetic dataset to compare FID, IS, and classical f-divergences, revealing that divergence-based metrics are more stable for model evaluation.

Eyal Betzalel, Coby Penso, Aviv Navon et al.

Proposes a global planning framework combining local smoothing of contact models with RRT, achieving efficient contact-rich manipulation with less computation.

Tao Pang, H. J. Terry Suh, Lujie Yang et al.