155 citations · 369 across the 17 of their papers we have counts for
18 papers
EBind: a practical approach to space binding
Jim Broadbent, Felix Cohen, Frederik Hvilshøj +2
We simplify space binding by focusing on two core components, a single encoder per modality and high-quality data; enabling training state-of-the-art models on a single GPU in a fe…
Train on Validation (ToV): Fast data selection with applications to fine-tuning
Ayush Jain, Andrea Montanari, Eren Sasoglu
State-of-the-art machine learning often follows a two-stage process: ~pre-training on large, general-purpose datasets; ~fine-tuning on task-specific data. In fine-tuning…
Scaling laws for learning with real and surrogate data
Ayush Jain, Andrea Montanari, Eren Sasoglu
Collecting large quantities of high-quality data can be prohibitively expensive or impractical, and a bottleneck in machine learning. One may instead augment a small set of dat…
Sliding-Window Superposition Coding:Two-User Interference Channels
Lele Wang, Young-Han Kim, Chiao-Yi Chen +2
A low-complexity coding scheme is developed to achieve the rate region of maximum likelihood decoding for interference channels. As in the classical rate-splitting multiple access…
Polar Coding for Processes with Memory
Eren Sasoglu, Ido Tal
We study polar coding for stochastic processes with memory. For example, a process may be defined by the joint distribution of the input and output of a channel. The memory may be…
Reed-Muller Codes Achieve Capacity on Erasure Channels
Shrinivas Kudekar, Santhosh Kumar, Marco Mondelli +3
We introduce a new approach to proving that a sequence of deterministic linear codes achieves capacity on an erasure channel under maximum a posteriori decoding. Rather than relyin…