53 citations · 106 across the 16 of their papers we have counts for
24 papers
Level, Sharpness, and Corpus: Why Zero-Shot OOD Detector Rankings Do Not Transfer
Ignacio M. De la Jara, Cristian Rodriguez-Opazo, Stephen Gould +1
Selecting a zero-shot out-of-distribution (OOD) detector for a new deployment is typically based on benchmark rankings, implicitly assuming that the highest-ranked detector will tr…
The Quest for Winning Tickets in Low-Rank Adapters
Hamed Damirchi, Cristian Rodriguez-Opazo, Ehsan Abbasnejad +2
The Lottery Ticket Hypothesis (LTH) suggests that over-parameterized neural networks contain sparse subnetworks ("winning tickets") capable of matching full model performance when…
An empirical study of the effect of video encoders on Temporal Video Grounding
Ignacio M. De la Jara, Cristian Rodriguez-Opazo, Edison Marrese-Taylor +1
Temporal video grounding is a fundamental task in computer vision, aiming to localize a natural language query in a long, untrimmed video. It has a key role in the scientific commu…
Mysteries of the Deep: Role of Intermediate Representations in Out of Distribution Detection
I. M. De la Jara, C. Rodriguez-Opazo, D. Teney +2
Out-of-distribution (OOD) detection is essential for reliably deploying machine learning models in the wild. Yet, most methods treat large pre-trained models as monolithic encoders…
RandLoRA: Full-rank parameter-efficient fine-tuning of large models
Paul Albert, Frederic Z. Zhang, Hemanth Saratchandran +3
Low-Rank Adaptation (LoRA) and its variants have shown impressive results in reducing the number of trainable parameters and memory requirements of large transformer networks while…
Frame-wise Conditioning Adaptation for Fine-Tuning Diffusion Models in Text-to-Video Prediction
Zheyuan Liu, Junyan Wang, Zicheng Duan +2
Text-video prediction (TVP) is a downstream video generation task that requires a model to produce subsequent video frames given a series of initial video frames and text describin…