model compression

3283 papers

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MC

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knowledge distillation (3680) large language model (12755) neural network (6616) efficient computing (779) neural network optimization (1293) transfer learning (5442) convolutional neural network (4216) neural network pruning (265) language model (4573) parameter efficiency (415)

Papers

Codec-SUPERB: An In-Depth Analysis of Sound Codec Models ACL 2024

Cache & Distil: Optimising API Calls to Large Language Models ACL 2024

Incremental pre-training from smaller language models ACL 2024

PTQ4SAM: Post-Training Quantization for Segment Anything CVPR 2024

BilevelPruning: Unified Dynamic and Static Channel Pruning for Convolutional Neural Networks CVPR 2024

FREE: Faster and Better Data-Free Meta-Learning CVPR 2024

SinSR: Diffusion-Based Image Super-Resolution in a Single Step CVPR 2024

On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm CVPR 2024

PikeLPN: Mitigating Overlooked Inefficiencies of Low-Precision Neural Networks CVPR 2024

One-step Diffusion with Distribution Matching Distillation CVPR 2024

Class Incremental Learning with Multi-Teacher Distillation CVPR 2024

CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image Generation CVPR 2024

Efficient Multitask Dense Predictor via Binarization CVPR 2024

Privacy-Preserving Face Recognition Using Trainable Feature Subtraction CVPR 2024

Adaptive Feature-based Low-Rank Compression of Large Language Models via Bayesian Optimization EMNLP 2024

MiLoRA: Efficient Mixture of Low-Rank Adaptation for Large Language Models Fine-tuning EMNLP 2024

ATQ: Activation Transformation forWeight-Activation Quantization of Large Language Models EMNLP 2024

Hybrid Distillation from RBMT and NMT: Helsinki-NLP’s Submission to the Shared Task on Translation into Low-Resource Languages of Spain EMNLP 2024

MoE-I2: Compressing Mixture of Experts Models through Inter-Expert Pruning and Intra-Expert Low-Rank Decomposition EMNLP 2024

LoRASC: Expressive and Generalizable Low-rank Adaptation for Large Models via Slow Cascaded Learning EMNLP 2024

TransferCVLM: Transferring Cross-Modal Knowledge for Vision-Language Modeling EMNLP 2024

Further Compressing Distilled Language Models via Frequency-aware Partial Sparse Coding of Embeddings EMNLP 2024

Less is Fed More: Sparsity Reduces Feature Distortion in Federated Learning EMNLP 2024

STTATTS: Unified Speech-To-Text And Text-To-Speech Model EMNLP 2024

MTA4DPR: Multi-Teaching-Assistants Based Iterative Knowledge Distillation for Dense Passage Retrieval EMNLP 2024