Trishul Chilimbi
18 papers · 2014–2025 · 8 venues · across top CS/AI venues
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Research topics
Machine Learning
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Learning Types Β· Machine Learning
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Deep Learning
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Artificial Intelligence
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Learning Types Β· Deep Learning
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Natural Language Processing
(7)
Application Areas
(7)
Core AI
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Core Methods
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Applications Β· Natural Language Processing
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Keywords
contrastive learning
(6)
knowledge distillation
(5)
direct preference optimization
(3)
multi-task learning
(3)
mutual information
(3)
large language model
(3)
representation learning
(3)
video understanding
(2)
zero-shot learning
(2)
vision-language representation
(2)
preference data
(2)
semantic similarity
(2)
multimodal learning
(2)
cross-modal retrieval
(2)
pre-trained language model
(2)
information retrieval
(1)
question answering
(1)
natural language generation
(1)
few-shot learning
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visual reasoning
(1)
Papers
ALERT: An LLM-powered Benchmark for Automatic Evaluation of Recommendation Explanations
NAACL 2025
AutoMixAlign: Adaptive Data Mixing for Multi-Task Preference Optimization in LLMs
ACL 2025
CoLLM: A Large Language Model for Composed Image Retrieval
CVPR 2025
InfoPO: On Mutual Information Maximization for Large Language Model Alignment
NAACL 2025
M-LLM Based Video Frame Selection for Efficient Video Understanding
CVPR 2025
Evolutionary Contrastive Distillation for Language Model Alignment
EMNLP 2024
Robust Multi-Task Learning with Excess Risks
ICML 2024
VidLA: Video-Language Alignment at Scale
CVPR 2024
OssCSE: Overcoming Surface Structure Bias in Contrastive Learning for Unsupervised Sentence Embedding
EMNLP 2023
ReAugKD: Retrieval-Augmented Knowledge Distillation For Pre-trained Language Models
ACL 2023
Understanding and Constructing Latent Modality Structures in Multi-Modal Representation Learning
CVPR 2023
Asynchronous Convergence in Multi-Task Learning via Knowledge Distillation from Converged Tasks
NAACL 2022
DynaMaR: Dynamic Prompt with Mask Token Representation
EMNLP 2022
MICO: Selective Search with Mutual Information Co-training
COLING 2022
Multi-Modal Alignment Using Representation Codebook
CVPR 2022
Vision-Language Pre-Training With Triple Contrastive Learning
CVPR 2022
Why do We Need Large Batchsizes in Contrastive Learning? A Gradient-Bias Perspective
NeurIPS 2022
Project Adam: Building an Efficient and Scalable Deep Learning Training System
OSDI 2014