2 citations · 5 across the 14 of their papers we have counts for
7 papers · 1 filter
Proteus: Incremental Memory Activation for Long-Context Sequence Modeling
Reza Bayat, Ali Behrouz, Vahab Mirrokni +1
The quadratic cost of attention-based sequence models for long contexts has motivated a growing line of research on memory-based models that can compress context into a compact sta…
On-Policy Self-Distillation with Sampled Demonstrations Reduces Output Diversity
Andrei Liviu Nicolicioiu, Mohammad Pezeshki, Aaron Courville
On-policy self-distillation achieves strong pass@1 accuracy by using a single model as both teacher and student, with the teacher conditioned on a correct demonstration to provide…
Tapered Language Models
Reza Bayat, Ali Behrouz, Aaron Courville
Modern language models, including transformer, recurrent, and memory-based variants, share a common chassis: a stack of identical layers in which parameters are allocated uniformly…
Scaling Stick-Breaking Attention: An Efficient Implementation and In-depth Study
Shawn Tan, Songlin Yang, Aaron Courville +2
The self-attention mechanism traditionally relies on the softmax operator, necessitating positional embeddings like RoPE, or position biases to account for token order. But current…
Scattered Mixture-of-Experts Implementation
Shawn Tan, Yikang Shen, Rameswar Panda +1
We present ScatterMoE, an implementation of Sparse Mixture-of-Experts (SMoE) on GPUs. ScatterMoE builds upon existing implementations, and overcoming some of the limitations to imp…
Improving Compositional Generalization Using Iterated Learning and Simplicial Embeddings
Yi Ren, Samuel Lavoie, Mikhail Galkin +2
Compositional generalization, the ability of an agent to generalize to unseen combinations of latent factors, is easy for humans but hard for deep neural networks. A line of resear…
Group Robust Classification Without Any Group Information
Christos Tsirigotis, Joao Monteiro, Pau Rodriguez +2
Empirical risk minimization (ERM) is sensitive to spurious correlations in the training data, which poses a significant risk when deploying systems trained under this paradigm in h…