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#pre-training

6 curated events
papersTODAY 04:00 UTC

Multi-Resolution, Multi-Domain Pre-Training Framework Proposed for Traffic Forecasting

Researchers present a pre-training framework for spatio-temporal traffic data that handles multiple resolutions and domains rather than assuming a single homogeneous data paradigm. The work targets the heterogeneity of traffic datasets, which the authors identify as a barrier to large-scale modeling for intelligent transportation systems. It is an arXiv preprint describing the method and its motivation.

research/pre-training/spatio-temporal/transportation
papersTODAY 04:00 UTC

Study compares pre-training strategies for graph transformers in biochemistry

A new arXiv paper examines how different pre-training approaches affect graph transformer performance on biochemistry tasks. The authors report that pre-training with supervision, using computed molecular properties as labels, outperformed the other strategies tested. The finding comes from a set of comparative experiments run in that domain.

graph-neural-networks/pre-training/biochemistry/research
papersTODAY 04:00 UTC

Paper Revisits Scaling and Training Objectives for Procedural Audio Pre-training

A new arXiv preprint examines how procedural audio should be scaled when used as a data source for learning transferable audio representations. The authors also ask whether training choices originally developed on natural audio still hold when the source is procedurally generated. The work aims to clarify design principles for procedural audio pre-training, an area the authors say lacks settled guidance.

audio/pre-training/representation-learning/arxiv
papersTODAY 04:00 UTC

SynGhost backdoor attack targets pre-trained models across downstream tasks

Researchers present SynGhost, a backdoor attack that exploits vulnerabilities in pre-training data and training pipelines to implant triggers that persist across many downstream tasks. The method uses syntactic transfer to make the injected triggers hard to detect while remaining task-agnostic. The paper appears as an arXiv cs.AI cross-listing revision.

safety/backdoor/pre-training/security
papersSEP 12 04:00 UTC

Paper Questions Whether Few-Shot Learning Protocols Reflect True Few-Shot Conditions

A new arXiv paper argues that standard few-shot learning benchmarks may not measure what they claim. In typical setups, models are first trained on a large auxiliary dataset whose categories differ from the evaluation episodes but come from the same visual domain, so the target task is not genuinely novel. The authors call for closer scrutiny of these pre-training assumptions when interpreting few-shot results.

few-shot/pre-training/benchmarks/research
papersSEP 10 04:00 UTC

Edu-QuRating: Multi-Dimensional Educational Data Curation with Distilled Pairwise Judgements

Researchers have introduced Edu-QuRating, a data-filtering method for language-model pre-training that evaluates educational value across multiple dimensions instead of a single scalar score. The approach relies on distilled pairwise judgements to rank documents, giving finer-grained control when curating training corpora. The work argues that one-dimensional educational quality metrics can be too coarse for datasets with specific application needs.

data-curation/pre-training/language-models/education