Optimization
17 researchers · 10 papers · 4 projects · 4 builders
Researchers (17)
Jeff Dean
Google DeepMind
400,701 citations · h-index 124
Michael I. Jordan
UC Berkeley
310,000 citations · h-index 155
Andrew Ng
Stanford University / Coursera
283,000 citations · h-index 130
Bernhard Scholkopf
Max Planck Institute for Intelligent Systems
195,000 citations · h-index 120
Diederik P. Kingma
Google DeepMind
185,000 citations · h-index 30
Ruslan Salakhutdinov
Carnegie Mellon University
97,000 citations · h-index 85
Max Welling
University of Amsterdam / Microsoft Research
86,000 citations · h-index 88
Song Han
MIT
58,000 citations · h-index 62
Zhouchen Lin
Peking University
42,000 citations · h-index 68
Anima Anandkumar
Caltech / NVIDIA
42,000 citations · h-index 68
Michael Bronstein
University of Oxford
42,000 citations · h-index 62
Yarin Gal
University of Oxford
38,000 citations · h-index 45
Rong Jin
Alibaba DAMO Academy
28,000 citations · h-index 62
Ryan Adams
Princeton University
28,000 citations · h-index 52
Elad Hazan
Princeton University
22,000 citations · h-index 48
Suvrit Sra
MIT
22,000 citations · h-index 50
Yann Dauphin
Meta AI
22,000 citations · h-index 30
Papers (10)
Adam: A Method for Stochastic Optimization
Auto-Encoding Variational Bayes
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Neural Architecture Search with Reinforcement Learning
Geometric Deep Learning: Going Beyond Euclidean Data
Identifying and Attacking the Saddle Point Problem in High-Dimensional Non-Convex Optimization
Introduction to Online Convex Optimization
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
Projects (4)
Run:ai
GPU orchestration platform for AI workloads. Fractional GPUs, dynamic scheduling, and cluster management. Acquired by NVIDIA in 2024.
by Abid Javed
Xnor.ai (acquired by Apple)
Ultra-efficient edge AI. Ran deep learning on $5 solar-powered chips using binary neural networks. Acquired by Apple for $200M in 2020.
by Rory Finnegan
tinygrad
A tiny neural network framework. Under 1000 lines of code, supports GPU acceleration. The antithesis of PyTorch complexity.
by George Hotz
MosaicML / DBRX
Open-source efficient LLM training platform. DBRX is a 132B MoE model rivaling GPT-3.5. Acquired by Databricks for $1.3B.
by Jonathan Frankle