Search Results for author:
Found 6 papers, 5 papers with code
Is Mamba Capable of In-Context Learning?
State of the art foundation models such as GPT-4 perform surprisingly well at in-context learning (ICL), a variant of meta-learning concerning the learned ability to solve tasks during a neural network forward pass, exploiting contextual information provided as input to the model.
Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models
Generalized Additive Models (GAMs) have recently experienced a resurgence in popularity due to their interpretability, which arises from expressing the target value as a sum of non-linear transformations of the features.
Interpretable Reinforcement Learning via Neural Additive Models for Inventory Management
In this work, we focus on developing dynamic inventory ordering policies for a multi-echelon, i. e. multi-stage, supply chain.
Bayesian Optimization-based Combinatorial Assignment
In this paper, we address this shortcoming by presenting a Bayesian optimization-based combinatorial assignment (BOCA) mechanism.
Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarks
We show that surrogate NAS benchmarks can model the true performance of architectures better than tabular benchmarks (at a small fraction of the cost), that they lead to faithful estimates of how well different NAS methods work on the original non-surrogate benchmark, and that they can generate new scientific insight.
NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search
One-shot neural architecture search (NAS) has played a crucial role in making NAS methods computationally feasible in practice.
