3 accepted papers
In this paper, we propose an optimally structured gradient coding scheme to mitigate the straggler problem in distributed learning. Conventional gradient coding methods often assume homogeneous straggler models or rely on excessive data replication, limiting performance in real-world heterogeneous s…
This paper advances temporal reasoning within dynamically changing high-dimensional noisy observations, focusing on a latent space that characterizes the nonlinear dynamics of objects in their environment. We introduce the *Gated Inference Network* (GIN), an efficient approximate Bayesian inference…
A distributed beamforming technique at each user pair (transmitter-receiver) is proposed in a (n, K)-user interference channel where K user pairs are allowed to simultaneously communicate with each other among n user pairs (K ≪ n). Each transmitter sends a single spatial stream and each user pair mi…