79 citations · 180 across the 19 of their papers we have counts for
19 papers
Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion
Alexandru Meterez, Amir Joudaki, Francesco Orabona +3
Normalization layers are one of the key building blocks for deep neural networks. Several theoretical studies have shown that batch normalization improves the signal propagation, b…
Transformers learn to implement preconditioned gradient descent for in-context learning
Kwangjun Ahn, Xiang Cheng, Hadi Daneshmand +1
Several recent works demonstrate that transformers can implement algorithms like gradient descent. By a careful construction of weights, these works show that multiple layers of tr…
On the impact of activation and normalization in obtaining isometric embeddings at initialization
Amir Joudaki, Hadi Daneshmand, Francis Bach
In this paper, we explore the structure of the penultimate Gram matrix in deep neural networks, which contains the pairwise inner products of outputs corresponding to a batch of in…
Efficient displacement convex optimization with particle gradient descent
Hadi Daneshmand, Jason D. Lee, Chi Jin
Particle gradient descent, which uses particles to represent a probability measure and performs gradient descent on particles in parallel, is widely used to optimize functions of p…
On Bridging the Gap between Mean Field and Finite Width in Deep Random Neural Networks with Batch Normalization
Amir Joudaki, Hadi Daneshmand, Francis Bach
Mean field theory is widely used in the theoretical studies of neural networks. In this paper, we analyze the role of depth in the concentration of mean-field predictions, specific…
Polynomial-time Sparse Measure Recovery: From Mean Field Theory to Algorithm Design
Hadi Daneshmand, Francis Bach
Mean field theory has provided theoretical insights into various algorithms by letting the problem size tend to infinity. We argue that the applications of mean-field theory go bey…