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cs.LG2026
MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image
Alan Arazi, Eilam Shapira, Shoham Grunblat +8
Tabular Foundation Models have recently established the state of the art in supervised tabular learning, by leveraging pretraining to learn generalizable representations of numeric…
cs.LG2026
Retrieval from Within: An Intrinsic Capability of Attention-Based Models
Elad Hoffer, Yochai Blau, Edan Kinderman +3
Retrieval-augmented generation (RAG) typically treats retrieval and generation as separate systems. We ask whether an attention-based encoder-decoder can instead retrieve directly…
cs.LG2024
Accurate Neural Training with 4-bit Matrix Multiplications at Standard Formats
Brian Chmiel, Ron Banner, Elad Hoffer +2
Quantization of the weights and activations is one of the main methods to reduce the computational footprint of Deep Neural Networks (DNNs) training. Current methods enable 4-bit q…