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Neural Networks Trained to Solve Differential Equations Learn General Representations
Martin Magill, Faisal Qureshi, Hendrick de Haan
Neural Information Processing Systems http://nips.cc/
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Segment Anything without Supervision
The Segmentation Anything Model (SAM) requires labor-intensive data labeling. We present Unsupervised SAM (UnSAM) for promptable and automatic whole-image segmentation that does not require human annotations.
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processing enables human-like zero-shot generalization to random dot stimuli
Motion is a powerful cue that humans use to detect and segment visual objects.
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Dynamics of Supervised and Reinforcement Learning in the Non-Linear Perceptron
The ability of a brain or a neural network to efficiently learn depends crucially on both the task structure and the learning rule.
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