activity
20182022
most citedFinetune like you pretrain: Improved finetuning of zero-shot vision models

4 citations · 7 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV20224 cited

Finetune like you pretrain: Improved finetuning of zero-shot vision models

Sachin Goyal, Ananya Kumar, Sankalp Garg +2

Finetuning image-text models such as CLIP achieves state-of-the-art accuracies on a variety of benchmarks. However, recent works like WiseFT (Wortsman et al., 2021) and LP-FT (Kuma…

cs.LG2020

Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution

Sankalp Garg, Navodita Sharma, Woojeong Jin +1

Time series prediction is an important problem in machine learning. Previous methods for time series prediction did not involve additional information. With a lot of dynamic knowle…

cs.LG2020

Symbolic Network: Generalized Neural Policies for Relational MDPs

Sankalp Garg, Aniket Bajpai, Mausam

A Relational Markov Decision Process (RMDP) is a first-order representation to express all instances of a single probabilistic planning domain with possibly unbounded number of obj…

cs.LG20193 cited

Size Independent Neural Transfer for RDDL Planning

Sankalp Garg, Aniket Bajpai, Mausam

Neural planners for RDDL MDPs produce deep reactive policies in an offline fashion. These scale well with large domains, but are sample inefficient and time-consuming to train from…

cs.AI2018

Transfer of Deep Reactive Policies for MDP Planning

Aniket Bajpai, Sankalp Garg, Mausam

Domain-independent probabilistic planners input an MDP description in a factored representation language such as PPDDL or RDDL, and exploit the specifics of the representation for…