
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients
Most popular optimizers for deep learning can be broadly categorized as ...
read it

Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE
Neural ordinary differential equations (NODEs) have recently attracted i...
read it

Sparse Regression Codes
Developing computationallyefficient codes that approach the Shannonthe...
read it

Zeroshot Transfer Learning for Semantic Parsing
While neural networks have shown impressive performance on large dataset...
read it

Sequence to Logic with Copy and Cache
Generating logical form equivalents of human language is a fresh way to ...
read it

Identifiability in Gaussian Graphical Models
In highdimensional graph learning problems, some topological properties...
read it

The TimeInvariant Multidimensional Gaussian Sequential RateDistortion Problem Revisited
We revisit the sequential ratedistortion (SRD) tradeoff problem for ve...
read it

Scalefree network optimization: foundations and algorithms
We investigate the fundamental principles that drive the development of ...
read it

Loopy Belief Propogation and Gibbs Measures
We address the question of convergence in the loopy belief propagation (...
read it

Lossy Compression via Sparse Linear Regression: Computationally Efficient Encoding and Decoding
We propose computationally efficient encoders and decoders for lossy com...
read it

MessagePassing Algorithms for Quadratic Minimization
Gaussian belief propagation (GaBP) is an iterative algorithm for computi...
read it

Lossy Compression via Sparse Linear Regression: Performance under Minimumdistance Encoding
We study a new class of codes for lossy compression with the squarederr...
read it

MessagePassing Algorithms: Reparameterizations and Splittings
The maxproduct algorithm, a local messagepassing scheme that attempts ...
read it
Sekhar Tatikonda
is this you? claim profile