1.1k citations · 1.2k across the 11 of their papers we have counts for
12 papers
ARC-AGI Without Pretraining
Isaac Liao, Albert Gu
Conventional wisdom in the age of LLMs dictates that solving IQ-test-like visual puzzles from the ARC-AGI-1 benchmark requires capabilities derived from massive pretraining. To cou…
Joint Distillation for Fast Likelihood Evaluation and Sampling in Flow-based Models
Xinyue Ai, Yutong He, Albert Gu +4
Log-likelihood evaluation enables important capabilities in generative models, including model comparison, certain fine-tuning objectives, and many downstream applications. Yet par…
Lyra: An Efficient and Expressive Subquadratic Architecture for Modeling Biological Sequences
Krithik Ramesh, Sameed M. Siddiqui, Albert Gu +2
Deep learning architectures such as convolutional neural networks and Transformers have revolutionized biological sequence modeling, with recent advances driven by scaling up found…
HybriDNA: A Hybrid Transformer-Mamba2 Long-Range DNA Language Model
Mingqian Ma, Guoqing Liu, Chuan Cao +12
Advances in natural language processing and large language models have sparked growing interest in modeling DNA, often referred to as the "language of life". However, DNA modeling…
Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models
Aviv Bick, Kevin Y. Li, Eric P. Xing +2
Transformer architectures have become a dominant paradigm for domains like language modeling but suffer in many inference settings due to their quadratic-time self-attention. Recen…
Hydra: Bidirectional State Space Models Through Generalized Matrix Mixers
Sukjun Hwang, Aakash Lahoti, Tri Dao +1
A wide array of sequence models are built on a framework modeled after Transformers, comprising alternating sequence mixer and channel mixer layers. This paper studies a unifying m…
An Empirical Study of Mamba-based Language Models
Roger Waleffe, Wonmin Byeon, Duncan Riach +13
Selective state-space models (SSMs) like Mamba overcome some of the shortcomings of Transformers, such as quadratic computational complexity with sequence length and large inferenc…
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
Tri Dao, Albert Gu
While Transformers have been the main architecture behind deep learning's success in language modeling, state-space models (SSMs) such as Mamba have recently been shown to match or…
Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling
Yair Schiff, Chia-Hsiang Kao, Aaron Gokaslan +3
Large-scale sequence modeling has sparked rapid advances that now extend into biology and genomics. However, modeling genomic sequences introduces challenges such as the need to mo…
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Soham De, Samuel L. Smith, Anushan Fernando +14
Recurrent neural networks (RNNs) have fast inference and scale efficiently on long sequences, but they are difficult to train and hard to scale. We propose Hawk, an RNN with gated…
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Albert Gu, Tri Dao
Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module. Many…
Augmenting conformers with structured state-space sequence models for online speech recognition
Haozhe Shan, Albert Gu, Zhong Meng +3
Online speech recognition, where the model only accesses context to the left, is an important and challenging use case for ASR systems. In this work, we investigate augmenting neur…