Chronicle is the first model jointly pretrained from scratch on text and time series in a unified transformer that matches a comparable language model on NLU tasks and sets new bars for time series classification and multimodal forecasting.
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SDG-MoE introduces learned signed interaction graphs and disagreement-gated deliberation among experts in MoE architectures, yielding 19.8% better validation perplexity than the strongest baseline.
TRACE is an autoregressive EEG pre-training framework using temporally adaptive cross-channel expert routing to learn transferable representations, achieving best results on several of eight downstream benchmarks.
citing papers explorer
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Chronicle: A Multimodal Foundation Model for Joint Language and Time Series Understanding
Chronicle is the first model jointly pretrained from scratch on text and time series in a unified transformer that matches a comparable language model on NLU tasks and sets new bars for time series classification and multimodal forecasting.
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SDG-MoE: Signed Debate Graph Mixture-of-Experts
SDG-MoE introduces learned signed interaction graphs and disagreement-gated deliberation among experts in MoE architectures, yielding 19.8% better validation perplexity than the strongest baseline.
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TRACE: Temporal Routing with Autoregressive Cross-channel Experts for EEG Representation Learning
TRACE is an autoregressive EEG pre-training framework using temporally adaptive cross-channel expert routing to learn transferable representations, achieving best results on several of eight downstream benchmarks.