RL-SPH is a reinforcement learning start primal heuristic that independently produces feasible solutions for ILPs with non-binary integers at 100% rate and with 28.6× lower primal gap than prior start heuristics.
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XAttnMark is a new neural audio watermarking method using partial parameter sharing, cross-attention for message retrieval, temporal conditioning, and a psychoacoustic TF masking loss that reports state-of-the-art detection and attribution robustness.
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RL-SPH: Learning to Achieve Feasible Solutions for Integer Linear Programs
RL-SPH is a reinforcement learning start primal heuristic that independently produces feasible solutions for ILPs with non-binary integers at 100% rate and with 28.6× lower primal gap than prior start heuristics.
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XAttnMark: Learning Robust Audio Watermarking with Cross-Attention
XAttnMark is a new neural audio watermarking method using partial parameter sharing, cross-attention for message retrieval, temporal conditioning, and a psychoacoustic TF masking loss that reports state-of-the-art detection and attribution robustness.