Technology
Self-SupervisedAggregationofDiverseExpertsfor Test-AgnosticLong-Tailed Recognition
Existing long-tailed recognition methods, aiming totrain class-balanced models from long-tailed data, generally assume the models would be evaluated on the uniform test class distribution.
SNIPER: Efficient Multi-Scale Training
Bharat Singh, Mahyar Najibi, Larry S. Davis
Neural Information Processing Systems http://nips.cc/
Residual Flows for Invertible Generative Modeling
Tian Qi Chen, Jens Behrmann, David K. Duvenaud, Joern-Henrik Jacobsen
Neural Information Processing Systems http://nips.cc/
Implicit Regularization for Optimal Sparse Recovery
Tomas Vaskevicius, Varun Kanade, Patrick Rebeschini
Ifwecanset1 /w max ฮท 1 / (20 w max) thentheiterationcomplexityof Theorem 1 reduces to O ( ฮบeff( w ) logฮฑ 1). Themagnitudeofw max is, however, anunknownquantity.
Heterogeneous Multi-output Gaussian Process Prediction
Pablo Moreno-Muรฑoz, Antonio Artรฉs, Mauricio รlvarez
Neural Information Processing Systems http://nips.cc/
dc3ca8bcd613e43ce540352b58d55d6d-Paper-Conference.pdf
Deep networks are increasingly used as models of cortical computation, particularly of the visual pathway.
On the Target-kernel Alignment: a Unified Analysis with Kernel Complexity
Y et, under the strongly-aligned regime, KM suffers the saturation effect, while TKM can be continuously improved as the alignment becomes stronger. This further implies that TKM has a strong ability to capture the strong alignment and provide a theoretically guaranteed solution to eliminate the phenomena of saturation effect.
