328 citations · 1.6k across the 61 of their papers we have counts for
9 papers · 1 filter
Unlearning via Sparse Representations
Vedant Shah, Frederik Träuble, Ashish Malik +5
Machine \emph{unlearning}, which involves erasing knowledge about a \emph{forget set} from a trained model, can prove to be costly and infeasible by existing techniques. We propose…
Physical Reasoning and Object Planning for Household Embodied Agents
Ayush Agrawal, Raghav Prabhakar, Anirudh Goyal +1
In this study, we explore the sophisticated domain of task planning for robust household embodied agents, with a particular emphasis on the intricate task of selecting substitute o…
Skill-Mix: a Flexible and Expandable Family of Evaluations for AI models
Dingli Yu, Simran Kaur, Arushi Gupta +3
With LLMs shifting their role from statistical modeling of language to serving as general-purpose AI agents, how should LLM evaluations change? Arguably, a key ability of an AI age…
A Theory for Emergence of Complex Skills in Language Models
Sanjeev Arora, Anirudh Goyal
A major driver of AI products today is the fact that new skills emerge in language models when their parameter set and training corpora are scaled up. This phenomenon is poorly und…
Cycle Consistency Driven Object Discovery
Aniket Didolkar, Anirudh Goyal, Yoshua Bengio
Developing deep learning models that effectively learn object-centric representations, akin to human cognition, remains a challenging task. Existing approaches facilitate object di…
Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior
Ayush Chakravarthy, Trang Nguyen, Anirudh Goyal +2
The aim of object-centric vision is to construct an explicit representation of the objects in a scene. This representation is obtained via a set of interchangeable modules called \…
DiscoGen: Learning to Discover Gene Regulatory Networks
Nan Rosemary Ke, Sara-Jane Dunn, Jorg Bornschein +11
Accurately inferring Gene Regulatory Networks (GRNs) is a critical and challenging task in biology. GRNs model the activatory and inhibitory interactions between genes and are inhe…
TC-VAE: Uncovering Out-of-Distribution Data Generative Factors
Cristian Meo, Anirudh Goyal, Justin Dauwels
Uncovering data generative factors is the ultimate goal of disentanglement learning. Although many works proposed disentangling generative models able to uncover the underlying gen…
Leveraging the Third Dimension in Contrastive Learning
Sumukh Aithal, Anirudh Goyal, Alex Lamb +2
Self-Supervised Learning (SSL) methods operate on unlabeled data to learn robust representations useful for downstream tasks. Most SSL methods rely on augmentations obtained by tra…