Diversity-aware memory policies improve test-time adaptation performance most under constrained memory budgets and challenging non-i.i.d. streams.
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4 Pith papers cite this work. Polarity classification is still indexing.
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MANGO combines gradient-gating and meta-learned regularization to balance stability and plasticity in single-pass online continual learning, reporting state-of-the-art accuracy on CLEAR-10, CIFAR-100, and Tiny-ImageNet.
StreamSampling.jl implements efficient one-pass sampling algorithms for data streams in Julia with constant memory footprint and performance gains over traditional methods.
Training data for open LLMs is systematically left-leaning, with pre-training corpora containing more political material than post-training data and model stances aligning with data distributions.
citing papers explorer
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GoTTA be Diverse: Rethinking Memory Policies for Test-Time Adaptation
Diversity-aware memory policies improve test-time adaptation performance most under constrained memory budgets and challenging non-i.i.d. streams.
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MANGO: Meta-Adaptive Network Gradient Optimization for Online Continual Learning
MANGO combines gradient-gating and meta-learned regularization to balance stability and plasticity in single-pass online continual learning, reporting state-of-the-art accuracy on CLEAR-10, CIFAR-100, and Tiny-ImageNet.
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StreamSampling.jl: Efficient Sampling from Data Streams in Julia
StreamSampling.jl implements efficient one-pass sampling algorithms for data streams in Julia with constant memory footprint and performance gains over traditional methods.
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What Is The Political Content in LLMs' Pre- and Post-Training Data?
Training data for open LLMs is systematically left-leaning, with pre-training corpora containing more political material than post-training data and model stances aligning with data distributions.