Zobrazeno 1 - 10
of 12 881
pro vyhledávání: '"Le A, T"'
Autor:
Carvalho, Joao, Le, An T., Jahr, Philipp, Sun, Qiao, Urain, Julen, Koert, Dorothea, Peters, Jan
Grasping objects successfully from a single-view camera is crucial in many robot manipulation tasks. An approach to solve this problem is to leverage simulation to create large datasets of pairs of objects and grasp poses, and then learn a conditiona
Externí odkaz:
http://arxiv.org/abs/2412.08398
Autor:
Le, An T., Hansel, Kay, Carvalho, João, Watson, Joe, Urain, Julen, Biess, Armin, Chalvatzaki, Georgia, Peters, Jan
Batch planning is increasingly crucial for the scalability of robotics tasks and dataset generation diversity. This paper presents Global Tensor Motion Planning (GTMP) -- a sampling-based motion planning algorithm comprising only tensor operations. W
Externí odkaz:
http://arxiv.org/abs/2411.19393
Autor:
Xu, Wenda, Han, Rujun, Wang, Zifeng, Le, Long T., Madeka, Dhruv, Li, Lei, Wang, William Yang, Agarwal, Rishabh, Lee, Chen-Yu, Pfister, Tomas
Recent advances in knowledge distillation (KD) have enabled smaller student models to approach the performance of larger teacher models. However, popular methods such as supervised KD and on-policy KD, are adversely impacted by the knowledge gaps bet
Externí odkaz:
http://arxiv.org/abs/2410.11325
Autor:
Watson, Joe, Song, Chen, Weeger, Oliver, Gruner, Theo, Le, An T., Hansel, Kay, Hendawy, Ahmed, Arenz, Oleg, Trojak, Will, Cranmer, Miles, D'Eramo, Carlo, Bülow, Fabian, Goyal, Tanmay, Peters, Jan, Hoffman, Martin W.
This survey examines the broad suite of methods and models for combining machine learning with physics knowledge for prediction and forecast, with a focus on partial differential equations. These methods have attracted significant interest due to the
Externí odkaz:
http://arxiv.org/abs/2408.09840
With the widespread adoption of Large Language Models (LLMs), concerns about potential misuse have emerged. To this end, watermarking has been adapted to LLM, enabling a simple and effective way to detect and monitor generated text. However, while th
Externí odkaz:
http://arxiv.org/abs/2407.13803
The material removal rates during milling operations are affected by the selection of the cutting depth and spindle speed. Poor selection of these parameters can result in chatter or suboptimal material removal rates. Stability Lobe Diagrams (SLDs) a
Externí odkaz:
http://arxiv.org/abs/2407.10202
We propose a forward-backward splitting dynamical system for solving inclusion problems of the form $0\in A(x)+B(x)$ in Hilbert spaces, where $A$ is a maximal operator and $B$ is a single-valued operator. Involved operators are assumed to satisfy a g
Externí odkaz:
http://arxiv.org/abs/2407.08139
Autor:
Nguyen, Duy M. H., Le, An T., Nguyen, Trung Q., Diep, Nghiem T., Nguyen, Tai, Duong-Tran, Duy, Peters, Jan, Shen, Li, Niepert, Mathias, Sonntag, Daniel
Prompt learning methods are gaining increasing attention due to their ability to customize large vision-language models to new domains using pre-trained contextual knowledge and minimal training data. However, existing works typically rely on optimiz
Externí odkaz:
http://arxiv.org/abs/2407.04489
Autor:
Hsieh, Cheng-Yu, Chuang, Yung-Sung, Li, Chun-Liang, Wang, Zifeng, Le, Long T., Kumar, Abhishek, Glass, James, Ratner, Alexander, Lee, Chen-Yu, Krishna, Ranjay, Pfister, Tomas
Large language models (LLMs), even when specifically trained to process long input contexts, struggle to capture relevant information located in the middle of their input. This phenomenon has been known as the lost-in-the-middle problem. In this work
Externí odkaz:
http://arxiv.org/abs/2406.16008
Autor:
Hsu, I-Hung, Wang, Zifeng, Le, Long T., Miculicich, Lesly, Peng, Nanyun, Lee, Chen-Yu, Pfister, Tomas
Grounded generation aims to equip language models (LMs) with the ability to produce more credible and accountable responses by accurately citing verifiable sources. However, existing methods, by either feeding LMs with raw or preprocessed materials,
Externí odkaz:
http://arxiv.org/abs/2406.05365