ALiBi bias is the expectation of positional LSH-induced block masks, yielding spectral and max-norm approximation bounds that reduce long-context biased attention to randomized short-context unbiased attention.
International Conference on Learning Representations (ICLR) , year=
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SOAP and its generalizations with arbitrary orthogonal projections converge at a provable rate when the projections are conditionally independent of the current gradient.
DANCE frames EEG event identification as a set-prediction problem to jointly detect and classify events directly from raw, unaligned signals, outperforming existing methods on seizure monitoring and matching onset-informed models on BCI tasks across ten datasets.
BoostAPR boosts automated program repair by training a sequence-level assessor and line-level credit allocator from execution outcomes, then applying them in PPO to reach 40.7% on SWE-bench Verified.
ECTraj enhances consistency models for multi-agent trajectory prediction via improved student-teacher supervision and conditional top-K generation, yielding faster inference and competitive accuracy on Argoverse 2.
bispectrum library delivers selective G-bispectra for seven groups with reduced costs (O(|G|) for finite groups, O(L^2) for spheres), sub-millisecond GPU times, and superior benchmark performance versus standard pooling in low-data regimes.
C-BPO personalizes LLMs via preference-calibrated binary signals and PU learning theory to isolate inter-user differences from shared task knowledge.
citing papers explorer
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Positional LSH: Binary Block Matrix Approximation for Attention with Linear Biases
ALiBi bias is the expectation of positional LSH-induced block masks, yielding spectral and max-norm approximation bounds that reduce long-context biased attention to randomized short-context unbiased attention.
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Convergence Rate Analysis of SOAP with Arbitrary Orthogonal Projection Matrices
SOAP and its generalizations with arbitrary orthogonal projections converge at a provable rate when the projections are conditionally independent of the current gradient.
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DANCE: Detect and Classify Events in EEG
DANCE frames EEG event identification as a set-prediction problem to jointly detect and classify events directly from raw, unaligned signals, outperforming existing methods on seizure monitoring and matching onset-informed models on BCI tasks across ten datasets.
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BoostAPR: Boosting Automated Program Repair via Execution-Grounded Reinforcement Learning with Dual Reward Models
BoostAPR boosts automated program repair by training a sequence-level assessor and line-level credit allocator from execution outcomes, then applying them in PPO to reach 40.7% on SWE-bench Verified.
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Enhancing Consistency Models for Multi-Agent Trajectory Prediction
ECTraj enhances consistency models for multi-agent trajectory prediction via improved student-teacher supervision and conditional top-K generation, yielding faster inference and competitive accuracy on Argoverse 2.
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bispectrum: Selective $G$-Bispectra Made Practical
bispectrum library delivers selective G-bispectra for seven groups with reduced costs (O(|G|) for finite groups, O(L^2) for spheres), sub-millisecond GPU times, and superior benchmark performance versus standard pooling in low-data regimes.
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Personalizing LLMs with Binary Feedback: A Preference-Corrected Optimization Framework
C-BPO personalizes LLMs via preference-calibrated binary signals and PU learning theory to isolate inter-user differences from shared task knowledge.