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Found 16 papers, 10 papers with code
Guiding Language Model Math Reasoning with Planning Tokens
Large language models (LLMs) have recently attracted considerable interest for their ability to perform complex reasoning tasks, such as chain-of-thought reasoning.
Re-Weighted Softmax Cross-Entropy to Control Forgetting in Federated Learning
In Federated Learning, a global model is learned by aggregating model updates computed at a set of independent client nodes, to reduce communication costs multiple gradient steps are performed at each node prior to aggregation.
Building a Subspace of Policies for Scalable Continual Learning
We introduce Continual Subspace of Policies (CSP), a new approach that incrementally builds a subspace of policies for training a reinforcement learning agent on a sequence of tasks.
Multi-Head Adapter Routing for Cross-Task Generalization
We find that routing is most beneficial during multi-task pre-training rather than during few-shot adaptation and propose $\texttt{MHR}$-$\mu$, which discards routing and fine-tunes the average of the pre-trained adapters on each downstream tasks.
New Insights on Reducing Abrupt Representation Change in Online Continual Learning
In this work, we focus on the change in representations of observed data that arises when previously unobserved classes appear in the incoming data stream, and new classes must be distinguished from previous ones.
On Anytime Learning at Macroscale
Practitioners have then to decide how to allocate their computational budget in order to obtain the best performance at any point in time.
SPeCiaL: Self-Supervised Pretraining for Continual Learning
This paper presents SPeCiaL: a method for unsupervised pretraining of representations tailored for continual learning.
Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning
With the use of a replay buffer we show that this approach can be extended to asynchronous settings, where modules can operate and continue to update with possibly large communication delays.
New Insights on Reducing Abrupt Representation Change in Online Continual Learning
In this work, we focus on the change in representations of observed data that arises when previously unobserved classes appear in the incoming data stream, and new classes must be distinguished from previous ones.
Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning
We propose Continual-MAML, an online extension of the popular MAML algorithm as a strong baseline for this scenario.
Online Learned Continual Compression with Adaptive Quantization Modules
We show how to use discrete auto-encoders to effectively address this challenge and introduce Adaptive Quantization Modules (AQM) to control variation in the compression ability of the module at any given stage of learning.
Online Learned Continual Compression with Stacked Quantization Modules
We first replace the episodic memory used in Experience Replay with SQM, leading to significant gains on standard continual learning benchmarks using a fixed memory budget.
Online Continual Learning with Maximally Interfered Retrieval
Methods based on replay, either generative or from a stored memory, have been shown to be effective approaches for continual learning, matching or exceeding the state of the art in a number of standard benchmarks.
Recurrent Value Functions
Despite recent successes in Reinforcement Learning, value-based methods often suffer from high variance hindering performance.
Deep Generative Modeling of LiDAR Data
In this work, we show that one can adapt deep generative models for this task by unravelling lidar scans into a 2D point map.
Language GANs Falling Short
Generating high-quality text with sufficient diversity is essential for a wide range of Natural Language Generation (NLG) tasks.
