Portrait of Sangnie Bhardwaj is unavailable

Sangnie Bhardwaj

PhD - Université de Montréal
Supervisor
Guillaume Lajoie
Co-supervisor
Hugo Larochelle
Research Topics
Deep Learning

Publications

Does learning the right latent variables necessarily improve in-context learning?
Sarthak Mittal
Eric Elmoznino
Leo Gagnon
Sangnie Bhardwaj
Dhanya Sridhar
Guillaume Lajoie
Large autoregressive models like Transformers can solve tasks through in-context learning (ICL) without learning new weights, suggesting ave… (see more)nues for efficiently solving new tasks. For many tasks, e.g., linear regression, the data factorizes: examples are independent given a task latent that generates the data, e.g., linear coefficients. While an optimal predictor leverages this factorization by inferring task latents, it is unclear if Transformers implicitly do so or if they instead exploit heuristics and statistical shortcuts enabled by attention layers. Both scenarios have inspired active ongoing work. In this paper, we systematically investigate the effect of explicitly inferring task latents. We minimally modify the Transformer architecture with a bottleneck designed to prevent shortcuts in favor of more structured solutions, and then compare performance against standard Transformers across various ICL tasks. Contrary to intuition and some recent works, we find little discernible difference between the two; biasing towards task-relevant latent variables does not lead to better out-of-distribution performance, in general. Curiously, we find that while the bottleneck effectively learns to extract latent task variables from context, downstream processing struggles to utilize them for robust prediction. Our study highlights the intrinsic limitations of Transformers in achieving structured ICL solutions that generalize, and shows that while inferring the right latents aids interpretability, it is not sufficient to alleviate this problem.
2025-05-01
ICML.cc/2025/Conference (poster)
doi.org
openreview.net
Explicit Knowledge Factorization Meets In-Context Learning: What Do We Gain?
Sarthak Mittal
Eric Elmoznino
Leo Gagnon
Sangnie Bhardwaj
Dhanya Sridhar
Guillaume Lajoie
2024-03-05
ICLR.cc/2024/Workshop/R2-FM (poster)
openreview.net
openreview.net
Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency
Tianhong Li
Sangnie Bhardwaj
Yonglong Tian
Han Zhang
Jarred Barber
Dina Katabi
Guillaume Lajoie
Huiwen Chang
Dilip Krishnan
Current vision-language generative models rely on expansive corpora of paired image-text data to attain optimal performance and generalizati… (see more)on capabilities. However, automatically collecting such data (e.g. via large-scale web scraping) leads to low quality and poor image-text correlation, while human annotation is more accurate but requires significant manual effort and expense. We introduce
2024-01-16
ICLR.cc/2024/Conference (spotlight)
doi.org
openreview.net
Steerable Equivariant Representation Learning
Sangnie Bhardwaj
Willie McClinton
Tongzhou Wang
Guillaume Lajoie
Chen Sun
Phillip Isola
Dilip Krishnan
Pre-trained deep image representations are useful for post-training tasks such as classification through transfer learning, image retrieval,… (see more) and object detection. Data augmentations are a crucial aspect of pre-training robust representations in both supervised and self-supervised settings. Data augmentations explicitly or implicitly promote invariance in the embedding space to the input image transformations. This invariance reduces generalization to those downstream tasks which rely on sensitivity to these particular data augmentations. In this paper, we propose a method of learning representations that are instead equivariant to data augmentations. We achieve this equivariance through the use of steerable representations. Our representations can be manipulated directly in embedding space via learned linear maps. We demonstrate that our resulting steerable and equivariant representations lead to better performance on transfer learning and robustness: e.g. we improve linear probe top-1 accuracy by between 1% to 3% for transfer; and ImageNet-C accuracy by upto 3.4%. We further show that the steerability of our representations provides significant speedup (nearly 50x) for test-time augmentations; by applying a large number of augmentations for out-of-distribution detection, we significantly improve OOD AUC on the ImageNet-C dataset over an invariant representation.
2023-02-22
ArXiv (preprint)
doi.org
openreview.net