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AdaptiveStochasticVarianceReduction forNon-convexFinite-SumMinimization
Toourknowledge, ADASPIDER isthefirstparameterfree non-convex variance-reduction method in the sense that it does not require the knowledge of problem-dependent parameters, such as smoothness constant L,targetaccuracyฯตoranybound ongradient norms.
Statistical Estimation in the Spiked Tensor Model via the Quantum Approximate Optimization Algorithm
See Figure 1 for an illustration of the different SNR thresholds and Section 2.1 for more
Toward Robust Incomplete Multimodal Sentiment Analysis via Hierarchical Representation Learning Mingcheng Li1,3 Dingkang Y ang 1,3 Y ang Liu 1 Shunli Wang 1,3
To this end, we propose a Hierarchical Representation Learning Framework (HRLF) for the MSA task under uncertain missing modalities.
MaskTune: MitigatingSpuriousCorrelationsby ForcingtoExplore
This workproposesMaskTune, a masking strategy that prevents over-reliance on spurious (or a limited number of) features. MaskTuneforces the trained model to explore new features during asingleepochfinetuning bymasking previously discoveredfeatures.MaskTune, unlike earlier approaches for mitigating shortcut learning, does not require any supervision, suchasannotating spurious features orlabels forsubgroup samples in a dataset.
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We show it depends on the precise way in which the limit is taken, and in particular on how the quantityofdata,thehiddenlayerwidth,&thelearningratescalesasd .
