Paper Reading

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Paper Reading

Network Pruning

Learning both Weights and Connections for Efficient Neural Network

2015

Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

ICLR

2015-10

Rethinking the Value of Network Pruning

2018

The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

2018

A Simple and Effective Pruning Approach for Large Language Models

https://github.com/locuslab/wanda

2023-06

Federated Learning

Communication-Efficient Learning of Deep Networks from Decentralized Data

2017

How To Backdoor Federated Learning

AISTATS

2018-07

Analyzing Federated Learning through an Adversarial Lens

ICML

2018-11

Deep Leakage from Gradients

NeurIPS

2019-06

Advances and Open Problems in Federated Learning

Preprint

2019-10

FEDERATED OPTIMIZATION IN HETEROGENEOUS NETWORKS

2020

Performance Optimization for Federated Person Re-identification via Benchmark Analysis

2020

Inverting Gradients - How easy is it to break privacy in federated learning?

NeurIPS

2020-03

Model-Contrastive Federated Learning

CVPR

2021-05

Preservation of the Global Knowledge by Not-True Distillation in Federated Learning

NeurIPS

2021-06

FEDBABU: TOWARD ENHANCED REPRESENTATION FOR FEDERATED IMAGE CLASSIFICATION

ICLR

2021-06

Gradient Inversion with Generative Image Prior

NeurIPS

2021-08

Evaluating Gradient Inversion Attacks and Defenses in Federated Learning

NeurIPS

2021-11

Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling

EMNLP

Gradient Inversion

Inverting Gradients - How easy is it to break privacy in federated learning?

NeurIPS

2020-03

Gradient Inversion with Generative Image Prior

NeurIPS

2021-08

Evaluating Gradient Inversion Attacks and Defenses in Federated Learning

NeurIPS

2021-11

Knowledge Distillation

Distilling the Knowledge in a Neural Network

2015

LLM

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

2018

LLaMA: Open and Efficient Foundation Language Models

2023

Network Quantization

Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

ICLR

2015-10

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

2022-08

SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

ICML

2022-11

LLM-QAT: Data-Free Quantization Aware Training for Large Language Models

Preprint

2023-05

AWQ: Activation-aware Weight Quantization for LLM

Preprint

2023-06

SqueezeLLM: Dense-and-Sparse Quantization

Preprint

2023-06

PEFT

Parameter-Efficient Transfer Learning for NLP

ICML

2019-02

LoRA: Low-Rank Adaptation of Large Language Models

Preprint

2021-06

Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

NeurIPS

2022-05

QLoRA: Efficient Finetuning of Quantized LLMs

Preprint

2023-05

No description

Large Language Models Are Reasoning Teachers

ACL

2022

Revisiting Intermediate Layer Distillation for Compressing Language Models: An Overfitting Perspective

Findings of EACL

2023

Big Little Transformer Decoder

2023-02

SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference

2023-07