Amir Gholami
27 papers · 2018–2025 · 8 conferences · across top CS/AI conferences
Achievements
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(39)
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(8)
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(7)
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(26)
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(2)
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(10)
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Topic Evolution
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Conferences
NIPS (10)
ICML (7)
AAAI (3)
ACL (2)
CVPR (2)
EMNLP (1)
ICCV (1)
WACV (1)
Top co-authors
Keywords
model compression
(8)
neural network optimization
(6)
inference efficiency
(4)
mixed-precision quantization
(4)
model quantization
(4)
large language model
(3)
neural network quantization
(3)
scientific machine learning
(2)
hessian spectrum
(2)
batch normalization
(2)
hessian analysis
(2)
transformer architecture
(2)
neural network
(2)
knowledge distillation
(2)
data augmentation
(2)
partial differential equation
(2)
large batch training
(2)
object detection
(1)
curriculum learning
(1)
k-means clustering
(1)
Papers
Squeezed Attention: Accelerating Long Context Length LLM Inference
ACL 2025
QuantSpec: Self-Speculative Decoding with Hierarchical Quantized KV Cache
ICML 2025
Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks
ICML 2025
SqueezeLLM: Dense-and-Sparse Quantization
ICML 2024
KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization
NIPS 2024
LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement
ACL 2024
An LLM Compiler for Parallel Function Calling
ICML 2024
TinyAgent: Function Calling at the Edge
EMNLP 2024
Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior
NIPS 2023
Speculative Decoding with Big Little Decoder
NIPS 2023
Hessian-Aware Pruning and Optimal Neural Implant
WACV 2022
Squeezeformer: An Efficient Transformer for Automatic Speech Recognition
NIPS 2022
A Fast Post-Training Pruning Framework for Transformers
NIPS 2022
ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning
AAAI 2021
I-BERT: Integer-only BERT Quantization
ICML 2021
HAWQ-V3: Dyadic Neural Network Quantization
ICML 2021
Characterizing possible failure modes in physics-informed neural networks
NIPS 2021
Inefficiency of K-FAC for Large Batch Size Training
AAAI 2020
Boundary thickness and robustness in learning models
NIPS 2020
HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks
NIPS 2020
Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT
AAAI 2020
ZeroQ: A Novel Zero Shot Quantization Framework
CVPR 2020
PowerNorm: Rethinking Batch Normalization in Transformers
ICML 2020
HAWQ: Hessian AWare Quantization of Neural Networks With Mixed-Precision
ICCV 2019
Trust Region Based Adversarial Attack on Neural Networks
CVPR 2019
ANODEV2: A Coupled Neural ODE Framework
NIPS 2019
Hessian-based Analysis of Large Batch Training and Robustness to Adversaries
NIPS 2018