Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Miller, Alexander H."'
Autor:
Hurley, Matthew J., Tanner, Christian P. N., Portner, Joshua, Utterback, James K., Coropceanu, Igor, Williams, Garth J., Das, Avishek, Fluerasu, Andrei, Sun, Yanwen, Song, Sanghoon, Hamerlynck, Leo M., Miller, Alexander H., Bhattacharyya, Priyadarshini, Talapin, Dmitri V., Ginsberg, Naomi S., Teitelbaum, Samuel W.
Solution-phase bottom up self-assembly of nanocrystals into superstructures such as ordered superlattices is an attractive strategy to generate functional materials of increasing complexity, including very recent advances that incorporate strong inte
Externí odkaz:
http://arxiv.org/abs/2401.06103
Autor:
Bakhtin, Anton, Wu, David J, Lerer, Adam, Gray, Jonathan, Jacob, Athul Paul, Farina, Gabriele, Miller, Alexander H, Brown, Noam
No-press Diplomacy is a complex strategy game involving both cooperation and competition that has served as a benchmark for multi-agent AI research. While self-play reinforcement learning has resulted in numerous successes in purely adversarial games
Externí odkaz:
http://arxiv.org/abs/2210.05492
Autor:
Küttler, Heinrich, Nardelli, Nantas, Miller, Alexander H., Raileanu, Roberta, Selvatici, Marco, Grefenstette, Edward, Rocktäschel, Tim
Progress in Reinforcement Learning (RL) algorithms goes hand-in-hand with the development of challenging environments that test the limits of current methods. While existing RL environments are either sufficiently complex or based on fast simulation,
Externí odkaz:
http://arxiv.org/abs/2006.13760
Autor:
Petroni, Fabio, Lewis, Patrick, Piktus, Aleksandra, Rocktäschel, Tim, Wu, Yuxiang, Miller, Alexander H., Riedel, Sebastian
When pre-trained on large unsupervised textual corpora, language models are able to store and retrieve factual knowledge to some extent, making it possible to use them directly for zero-shot cloze-style question answering. However, storing factual kn
Externí odkaz:
http://arxiv.org/abs/2005.04611
Autor:
Sivakumar, Viswanath, Delalleau, Olivier, Rocktäschel, Tim, Miller, Alexander H., Küttler, Heinrich, Nardelli, Nantas, Rabbat, Mike, Pineau, Joelle, Riedel, Sebastian
Effective network congestion control strategies are key to keeping the Internet (or any large computer network) operational. Network congestion control has been dominated by hand-crafted heuristics for decades. Recently, ReinforcementLearning (RL) ha
Externí odkaz:
http://arxiv.org/abs/1910.04054
Autor:
Petroni, Fabio, Rocktäschel, Tim, Lewis, Patrick, Bakhtin, Anton, Wu, Yuxiang, Miller, Alexander H., Riedel, Sebastian
Recent progress in pretraining language models on large textual corpora led to a surge of improvements for downstream NLP tasks. Whilst learning linguistic knowledge, these models may also be storing relational knowledge present in the training data,
Externí odkaz:
http://arxiv.org/abs/1909.01066
We investigate the impact of search strategies in neural dialogue modeling. We first compare two standard search algorithms, greedy and beam search, as well as our newly proposed iterative beam search which produces a more diverse set of candidate re
Externí odkaz:
http://arxiv.org/abs/1811.00907
Sequence generation models for dialogue are known to have several problems: they tend to produce short, generic sentences that are uninformative and unengaging. Retrieval models on the other hand can surface interesting responses, but are restricted
Externí odkaz:
http://arxiv.org/abs/1808.04776
Autor:
Yang, Zhilin, Zhang, Saizheng, Urbanek, Jack, Feng, Will, Miller, Alexander H., Szlam, Arthur, Kiela, Douwe, Weston, Jason
Contrary to most natural language processing research, which makes use of static datasets, humans learn language interactively, grounded in an environment. In this work we propose an interactive learning procedure called Mechanical Turker Descent (MT
Externí odkaz:
http://arxiv.org/abs/1711.07950
Autor:
Miller, Alexander H., Feng, Will, Fisch, Adam, Lu, Jiasen, Batra, Dhruv, Bordes, Antoine, Parikh, Devi, Weston, Jason
We introduce ParlAI (pronounced "par-lay"), an open-source software platform for dialog research implemented in Python, available at http://parl.ai. Its goal is to provide a unified framework for sharing, training and testing of dialog models, integr
Externí odkaz:
http://arxiv.org/abs/1705.06476