ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2407.17356
  4. Cited By
Gradient-based inference of abstract task representations for
  generalization in neural networks

Gradient-based inference of abstract task representations for generalization in neural networks

24 July 2024
Ali Hummos
Felipe del-Rio
Brabeeba Mien Wang
Julio Hurtado
Cristian B. Calderon
G. Yang
ArXivPDFHTML

Papers citing "Gradient-based inference of abstract task representations for generalization in neural networks"

5 / 5 papers shown
Title
Flexible task abstractions emerge in linear networks with fast and bounded units
Flexible task abstractions emerge in linear networks with fast and bounded units
Kai Sandbrink
Jan P. Bauer
A. Proca
Andrew M. Saxe
Christopher Summerfield
Ali Hummos
58
2
0
17 Jan 2025
Meta-Dynamical State Space Models for Integrative Neural Data Analysis
Meta-Dynamical State Space Models for Integrative Neural Data Analysis
Ayesha Vermani
Josue Nassar
Hyungju Jeon
Matthew Dowling
Il Memming Park
29
1
0
07 Oct 2024
The Power of Scale for Parameter-Efficient Prompt Tuning
The Power of Scale for Parameter-Efficient Prompt Tuning
Brian Lester
Rami Al-Rfou
Noah Constant
VPVLM
280
3,843
0
18 Apr 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
268
5,660
0
05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
247
9,134
0
06 Jun 2015
1