Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Lan, Janice"'
LLMs are typically trained to answer user questions or follow instructions similarly to how human experts respond. However, in the standard alignment framework they lack the basic ability of explicit thinking before answering. Thinking is important f
Externí odkaz:
http://arxiv.org/abs/2410.10630
Graph neural networks (GNNs) have been shown to be astonishingly capable models for molecular property prediction, particularly as surrogates for expensive density functional theory calculations of relaxed energy for novel material discovery. However
Externí odkaz:
http://arxiv.org/abs/2407.10844
Autor:
Lan, Janice, Palizhati, Aini, Shuaibi, Muhammed, Wood, Brandon M., Wander, Brook, Das, Abhishek, Uyttendaele, Matt, Zitnick, C. Lawrence, Ulissi, Zachary W.
Computational catalysis is playing an increasingly significant role in the design of catalysts across a wide range of applications. A common task for many computational methods is the need to accurately compute the adsorption energy for an adsorbate
Externí odkaz:
http://arxiv.org/abs/2211.16486
Autor:
Zitnick, C. Lawrence, Das, Abhishek, Kolluru, Adeesh, Lan, Janice, Shuaibi, Muhammed, Sriram, Anuroop, Ulissi, Zachary, Wood, Brandon
Modeling the energy and forces of atomic systems is a fundamental problem in computational chemistry with the potential to help address many of the world's most pressing problems, including those related to energy scarcity and climate change. These c
Externí odkaz:
http://arxiv.org/abs/2206.14331
Autor:
Tran, Richard, Lan, Janice, Shuaibi, Muhammed, Wood, Brandon M., Goyal, Siddharth, Das, Abhishek, Heras-Domingo, Javier, Kolluru, Adeesh, Rizvi, Ammar, Shoghi, Nima, Sriram, Anuroop, Therrien, Felix, Abed, Jehad, Voznyy, Oleksandr, Sargent, Edward H., Ulissi, Zachary, Zitnick, C. Lawrence
The development of machine learning models for electrocatalysts requires a broad set of training data to enable their use across a wide variety of materials. One class of materials that currently lacks sufficient training data is oxides, which are cr
Externí odkaz:
http://arxiv.org/abs/2206.08917
Autor:
Dathathri, Sumanth, Madotto, Andrea, Lan, Janice, Hung, Jane, Frank, Eric, Molino, Piero, Yosinski, Jason, Liu, Rosanne
Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities. However, controlling attributes of the generated language (e.g. switching topic or sentiment) is difficult without modifying t
Externí odkaz:
http://arxiv.org/abs/1912.02164
Autor:
Moskovitz, Ted, Wang, Rui, Lan, Janice, Kapoor, Sanyam, Miconi, Thomas, Yosinski, Jason, Rawal, Aditya
Standard gradient descent methods are susceptible to a range of issues that can impede training, such as high correlations and different scaling in parameter space.These difficulties can be addressed by second-order approaches that apply a pre-condit
Externí odkaz:
http://arxiv.org/abs/1910.08461
Neural networks enjoy widespread use, but many aspects of their training, representation, and operation are poorly understood. In particular, our view into the training process is limited, with a single scalar loss being the most common viewport into
Externí odkaz:
http://arxiv.org/abs/1909.01440
The recent "Lottery Ticket Hypothesis" paper by Frankle & Carbin showed that a simple approach to creating sparse networks (keeping the large weights) results in models that are trainable from scratch, but only when starting from the same initial wei
Externí odkaz:
http://arxiv.org/abs/1905.01067
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