Hadi Daneshmand
16 papers · 2014–2025 · 4 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
Jump to papers ↓+2 more ↓ Show less ↑
🌉 Interdisciplinary Bridge · Bronze 🌍 Conference Polyglot · Bronze 🏃 Academic Marathon · Half 🗺️ Taxonomy Completionist (22) 🐣 Hot Topic Early Bird
💎
Century Club · Bronze
🗃️
Keyword Collector
(81)
Venues
Top co-authors
Research topics
Machine Learning
(15)
Optimization & Theory · Machine Learning
(14)
Deep Learning
(10)
Mathematics & Optimization
(7)
Optimization
(6)
Optimization & Theory · Deep Learning
(6)
Techniques
(6)
Mathematics
(3)
Architectures
(2)
Core Methods
(2)
Artificial Intelligence
(1)
Learning Paradigms
(1)
Learning Types
(1)
Keywords
batch normalization
(5)
stochastic gradient descent
(3)
convex optimization
(2)
adaptive sampling
(2)
mean field theory
(2)
gram matrix
(2)
rank collapse
(2)
neural network training
(1)
iterative optimization
(1)
temporal difference learning
(1)
optimal transport
(1)
in-context learning
(1)
network inference
(1)
sample complexity
(1)
accelerated optimization
(1)
empirical risk minimization
(1)
neural network theory
(1)
non-convex optimization
(1)
loss landscape
(1)
online learning
(1)
Papers
Transformers Can Learn Temporal Difference Methods for In-Context Reinforcement Learning
ICLR 2025
Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion
ICLR 2024
Efficient displacement convex optimization with particle gradient descent
ICML 2023
On Bridging the Gap between Mean Field and Finite Width Deep Random Multilayer Perceptron with Batch Normalization
ICML 2023
On the impact of activation and normalization in obtaining isometric embeddings at initialization
NeurIPS 2023
Transformers learn to implement preconditioned gradient descent for in-context learning
NeurIPS 2023
Batch Normalization Orthogonalizes Representations in Deep Random Networks
NeurIPS 2021
Rethinking the Variational Interpretation of Accelerated Optimization Methods
NeurIPS 2021
Revisiting the Role of Euler Numerical Integration on Acceleration and Stability in Convex Optimization
AISTATS 2021
Batch normalization provably avoids ranks collapse for randomly initialised deep networks
NeurIPS 2020
Exponential convergence rates for Batch Normalization: The power of length-direction decoupling in non-convex optimization
AISTATS 2019
Local Saddle Point Optimization: A Curvature Exploitation Approach
AISTATS 2019
Escaping Saddles with Stochastic Gradients
ICML 2018
Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy
NeurIPS 2016
Starting Small - Learning with Adaptive Sample Sizes
ICML 2016
Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm
ICML 2014