Shinhyeok Hwang
Shinhyeok Hwang
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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