Government
Inferring stochastic low-rank recurrent neural networks from neural data Matthijs Pals 1,2 A Erdem Sa gtekin 1,2,3 Felix Pei 1,2 Manuel Gloeckler
However, it is unclear how to best fit low-rank RNNs to data consisting of noisy observations of an underlying stochastic system. Here, we propose to fit stochastic low-rank RNNs with variational sequential Monte Carlo methods.
Policy Optimization for Robust Average Cost MDPs
Specifically, we focus on ergodic Markov chains.
Supra-Laplacian Encoding for Transformer on Dynamic Graphs
Fully connected Graph Transformers (GT) have rapidly become prominent in the static graph community as an alternative to Message-Passing models, which suffer from a lack of expressivity, oversquashing, and under-reaching.
On the Noise Robustness of In-Context Learning for Text Generation
Large language models (LLMs) have shown impressive performance on downstream tasks by in-context learning (ICL), which heavily relies on the quality of demonstrations selected from a large set of annotated examples.
DPIC: Decoupling Prompt and Intrinsic Characteristics for LLM Generated Text Detection Xiao Y u 1,2, Y uang Qi
Consequently, detecting whether a text is generated by LLMs has become increasingly important.
Automated Efficient Estimation using Monte Carlo Efficient Influence Functions
ML or targeted minimum loss estimation.
Embedding-Aligned Language Models Guy Tennenholtz
In this paper, we present a novel framework which accomplishes this by exploiting latent embedding spaces to define an objective function for an LLM in an iterative RL-driven process. As an example, consider the challenge of assisting content creators in generating valuable content within a recommender ecosystem (e.g., Y ouTube, Reddit, Spotify) [Boutilier et al., 2024].