71 citations · 128 across the 18 of their papers we have counts for
9 papers · 1 filter
Query Efficient Structured Matrix Learning
Noah Amsel, Pratyush Avi, Tyler Chen +5
We study the problem of learning a structured approximation (low-rank, sparse, banded, etc.) to an unknown matrix given access to matrix-vector product (matvec) queries of the…
MARVIS: Modality Adaptive Reasoning over VISualizations
Benjamin Feuer, Lennart Purucker, Oussama Elachqar +1
Predictive applications of machine learning often rely on small (sub 1 Bn parameter) specialized models tuned to particular domains or modalities. Such models often achieve excelle…
OpenThoughts: Data Recipes for Reasoning Models
Etash Guha, Ryan Marten, Sedrick Keh +47
Reasoning models have made rapid progress on many benchmarks involving math, code, and science. Yet, there are still many open questions about the best training recipes for reasoni…
VeriThoughts: Enabling Automated Verilog Code Generation using Reasoning and Formal Verification
Patrick Yubeaton, Andre Nakkab, Weihua Xiao +4
This paper introduces VeriThoughts, a novel dataset designed for reasoning-based Verilog code generation. We establish a new benchmark framework grounded in formal verification met…
When Are Concepts Erased From Diffusion Models?
Kevin Lu, Nicky Kriplani, Rohit Gandikota +4
In concept erasure, a model is modified to selectively prevent it from generating a target concept. Despite the rapid development of new methods, it remains unclear how thoroughly…
SEAL: Semantic Aware Image Watermarking
Kasra Arabi, R. Teal Witter, Chinmay Hegde +1
Generative models have rapidly evolved to generate realistic outputs. However, their synthetic outputs increasingly challenge the clear distinction between natural and AI-generated…
SolidMark: Evaluating Image Memorization in Generative Models
Nicky Kriplani, Minh Pham, Gowthami Somepalli +2
Recent works have shown that diffusion models are able to memorize training images and emit them at generation time. However, the metrics used to evaluate memorization and its miti…
Huff-LLM: End-to-End Lossless Compression for Efficient LLM Inference
Patrick Yubeaton, Tareq Mahmoud, Shehab Naga +8
As they become more capable, large language models (LLMs) have continued to rapidly increase in size. This has exacerbated the difficulty in running state of the art LLMs on small,…
WILDCHAT-50M: A Deep Dive Into the Role of Synthetic Data in Post-Training
Benjamin Feuer, Chinmay Hegde
Language model (LLM) post-training, from DPO to distillation, can refine behaviors and unlock new skills, but the open science supporting these post-training techniques is still in…