56 citations · 257 across the 54 of their papers we have counts for
12 papers · 1 filter
Learning to Compose SuperWeights for Neural Parameter Allocation Search
Piotr Teterwak, Soren Nelson, Nikoli Dryden +3
Neural parameter allocation search (NPAS) automates parameter sharing by obtaining weights for a network given an arbitrary, fixed parameter budget. Prior work has two major drawba…
CLAMP: Contrastive LAnguage Model Prompt-tuning
Piotr Teterwak, Ximeng Sun, Bryan A. Plummer +2
Large language models (LLMs) have emerged as powerful general-purpose interfaces for many machine learning problems. Recent work has adapted LLMs to generative visual tasks like im…
Socratis: Are large multimodal models emotionally aware?
Katherine Deng, Arijit Ray, Reuben Tan +3
Existing emotion prediction benchmarks contain coarse emotion labels which do not consider the diversity of emotions that an image and text can elicit in humans due to various reas…
From Fake to Real: Pretraining on Balanced Synthetic Images to Prevent Spurious Correlations in Image Recognition
Maan Qraitem, Kate Saenko, Bryan A. Plummer
Visual recognition models are prone to learning spurious correlations induced by a biased training set where certain conditions (\eg, Indoors) are over-represented in certain c…
Multiscale Video Pretraining for Long-Term Activity Forecasting
Reuben Tan, Matthias De Lange, Michael Iuzzolino +4
Long-term activity forecasting is an especially challenging research problem because it requires understanding the temporal relationships between observed actions, as well as the v…
LNL+K: Enhancing Learning with Noisy Labels Through Noise Source Knowledge Integration
Siqi Wang, Bryan A. Plummer
Learning with noisy labels (LNL) aims to train a high-performing model using a noisy dataset. We observe that noise for a given class often comes from a limited set of categories,…