Search Results for author:
Found 10 papers, 5 papers with code
Fairness Practices in Industry: A Case Study in Machine Learning Teams Building Recommender Systems
The rapid proliferation of recommender systems necessitates robust fairness practices to address inherent biases.
M1: Towards Scalable Test-Time Compute with Mamba Reasoning Models
In this paper, we introduce a novel hybrid linear RNN reasoning model, M1, built on the Mamba architecture, which allows memory-efficient inference.
Thinking Slow, Fast: Scaling Inference Compute with Distilled Reasoners
Recent advancements have demonstrated that the performance of large language models (LLMs) can be significantly enhanced by scaling computational resources at test time.
The Mamba in the Llama: Distilling and Accelerating Hybrid Models
The resulting hybrid model, which incorporates a quarter of the attention layers, achieves performance comparable to the original Transformer in chat benchmarks and outperforms open-source hybrid Mamba models trained from scratch with trillions of tokens in both chat benchmarks and general benchmarks.
Entity Disambiguation via Fusion Entity Decoding
Existing generative approaches demonstrate improved accuracy compared to classification approaches under the standardized ZELDA benchmark.
Ranked #1 on
Entity Linking
on KORE50
(Micro-F1 strong metric)
MambaByte: Token-free Selective State Space Model
We propose MambaByte, a token-free adaptation of the Mamba SSM trained autoregressively on byte sequences.
JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning
Join order selection (JOS) is the problem of ordering join operations to minimize total query execution cost and it is the core NP-hard combinatorial optimization problem of query optimization.
Pretraining Without Attention
Even so, BiGS is able to match BERT pretraining accuracy on GLUE and can be extended to long-form pretraining of 4096 tokens without approximation.
Procrastinated Tree Search: Black-box Optimization with Delayed, Noisy, and Multi-Fidelity Feedback
In black-box optimization problems, we aim to maximize an unknown objective function, where the function is only accessible through feedbacks of an evaluation or simulation oracle.
A Hybrid Solution to improve Iteration Efficiency in the Distributed Learning
Currently, many machine learning algorithms contain lots of iterations.
