27 citations · 85 across the 17 of their papers we have counts for
7 papers · 1 filter
Evaluation Beyond Task Performance: Analyzing Concepts in AlphaZero in Hex
Charles Lovering, Jessica Zosa Forde, George Konidaris +2
AlphaZero, an approach to reinforcement learning that couples neural networks and Monte Carlo tree search (MCTS), has produced state-of-the-art strategies for traditional board gam…
One Venue, Two Conferences: The Separation of Chinese and American Citation Networks
Bingchen Zhao, Yuling Gu, Jessica Zosa Forde +1
At NeurIPS, American and Chinese institutions cite papers from each other's regions substantially less than they cite endogamously. We build a citation graph to quantify this divid…
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
Reduce, Reuse, Recycle: Improving Training Efficiency with Distillation
Cody Blakeney, Jessica Zosa Forde, Jonathan Frankle +2
Methods for improving the efficiency of deep network training (i.e. the resources required to achieve a given level of model quality) are of immediate benefit to deep learning prac…
Efficient Methods for Natural Language Processing: A Survey
Marcos Treviso, Ji-Ung Lee, Tianchu Ji +19
Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance mea…
Strengthening Subcommunities: Towards Sustainable Growth in AI Research
Andi Peng, Jessica Zosa Forde, Yonadav Shavit +1
AI's rapid growth has been felt acutely by scholarly venues, leading to growing pains within the peer review process. These challenges largely center on the inability of specific s…
Does DQN really learn? Exploring adversarial training schemes in Pong
Bowen He, Sreehari Rammohan, Jessica Forde +1
In this work, we study two self-play training schemes, Chainer and Pool, and show they lead to improved agent performance in Atari Pong compared to a standard DQN agent -- trained…