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Found 15 papers, 2 papers with code
Denoising Autoregressive Representation Learning
In this paper, we explore a new generative approach for learning visual representations.
Transformers for Supervised Online Continual Learning
Inspired by the in-context learning capabilities of transformers and their connection to meta-learning, we propose a method that leverages these strengths for online continual learning.
Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models
We consider the problem of online fine tuning the parameters of a language model at test time, also known as dynamic evaluation.
Kalman Filter for Online Classification of Non-Stationary Data
Non-stationarity over the linear predictor weights is modelled using a parameter drift transition density, parametrized by a coefficient that quantifies forgetting.
DiscoGen: Learning to Discover Gene Regulatory Networks
To accurately identify GRNs, perturbational data is required.
Evaluating Representations with Readout Model Switching
Although much of the success of Deep Learning builds on learning good representations, a rigorous method to evaluate their quality is lacking.
Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies
Offline RL algorithms try to address this issue by bootstrapping the learning process from existing logged data without needing to interact with the environment from the very beginning.
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research
A shared goal of several machine learning communities like continual learning, meta-learning and transfer learning, is to design algorithms and models that efficiently and robustly adapt to unseen tasks.
Sequential Learning Of Neural Networks for Prequential MDL
In the prequential formulation of MDL, the objective is to minimize the cumulative next-step log-loss when sequentially going through the data and using previous observations for parameter estimation.
Learning to Induce Causal Structure
The fundamental challenge in causal induction is to infer the underlying graph structure given observational and/or interventional data.
Prequential MDL for Causal Structure Learning with Neural Networks
Learning the structure of Bayesian networks and causal relationships from observations is a common goal in several areas of science and technology.
A study on the plasticity of neural networks
One aim shared by multiple settings, such as continual learning or transfer learning, is to leverage previously acquired knowledge to converge faster on the current task.
Small Data, Big Decisions: Model Selection in the Small-Data Regime
Highly overparametrized neural networks can display curiously strong generalization performance - a phenomenon that has recently garnered a wealth of theoretical and empirical research in order to better understand it.
Bidirectional Helmholtz Machines
We present a lower-bound for the likelihood of this model and we show that optimizing this bound regularizes the model so that the Bhattacharyya distance between the bottom-up and top-down approximate distributions is minimized.
Towards Biologically Plausible Deep Learning
Neuroscientists have long criticised deep learning algorithms as incompatible with current knowledge of neurobiology.
