Deep Learning
New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
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End-To-End Causal Effect Estimation from Unstructured Natural Language Data
Neural Information Processing Systems
Work done during internship at Meta AI. 38th Conference on Neural Information Processing Systems (NeurIPS 2024).
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TrajCLIP: Pedestrian Trajectory Prediction Method Using Contrastive Learning and Idempotent Networks
Neural Information Processing Systems
By pairing historical and future trajectories and applying contrastive learning on the encoded feature space, we enforce same-space consistency constraints.
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Many-Shot In-Context Learning Rishabh Agarwal, A vi Singh
Neural Information Processing Systems
LLMs excel at few-shot in-context learning (ICL) - learning from a few input-output examples ("shots") provided in context at inference, without any weight updates.
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