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Found 8 papers, 2 papers with code
Modality-specific Distillation
In this paper, we propose modality-specific distillation (MSD) to effectively transfer knowledge from a teacher on multimodal datasets.
Audiobox: Unified Audio Generation with Natural Language Prompts
Research communities have made great progress over the past year advancing the performance of large scale audio generative models for a single modality (speech, sound, or music) through adopting more powerful generative models and scaling data.
Ranked #1 on
Audio Generation
on AudioCaps
Learning Easily Updated General Purpose Text Representations with Adaptable Task-Specific Prefixes
Fine-tuning a large pre-trained language model for each downstream task causes computational burdens in the inference time due to several times of forward passes.
Defending Against Patch-based Backdoor Attacks on Self-Supervised Learning
It was shown that an adversary can poison a small part of the unlabeled data so that when a victim trains an SSL model on it, the final model will have a backdoor that the adversary can exploit.
FRAME: Evaluating Rationale-Label Consistency Metrics for Free-Text Rationales
Following how humans communicate, free-text rationales aim to use natural language to explain neural language model (LM) behavior.
UNIREX: A Unified Learning Framework for Language Model Rationale Extraction
An extractive rationale explains a language model's (LM's) prediction on a given task instance by highlighting the text inputs that most influenced the prediction.
Urca Cooling in Neutron Star Crusts and Oceans: Effects of Nuclear Excitations
The excited-state structure of atomic nuclei can modify nuclear processes in stellar environments.
Nuclear Theory High Energy Astrophysical Phenomena Solar and Stellar Astrophysics
MSD: Saliency-aware Knowledge Distillation for Multimodal Understanding
The idea aims at mimicking a teacher's modality-specific predictions by introducing auxiliary loss terms for each modality.
