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Found 14 papers, 4 papers with code
Sample-Specific Debiasing for Better Image-Text Models
Self-supervised representation learning on image-text data facilitates crucial medical applications, such as image classification, visual grounding, and cross-modal retrieval.
SynBench: Task-Agnostic Benchmarking of Pretrained Representations using Synthetic Data
Given a pretrained model, the representations of data synthesized from the Gaussian mixture are used to compare with our reference to infer the quality.
Visual Pre-training for Navigation: What Can We Learn from Noise?
One powerful paradigm in visual navigation is to predict actions from observations directly.
Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework
With the integrated framework, we achieve up to 6\% improvement on the standard accuracy and 17\% improvement on the robust accuracy.
Higher-Order Certification for Randomized Smoothing
We also provide a framework that generalizes the calculation for certification using higher-order information.
Hidden Cost of Randomized Smoothing
The fragility of modern machine learning models has drawn a considerable amount of attention from both academia and the public.
HOTCAKE: Higher Order Tucker Articulated Kernels for Deeper CNN Compression
The emerging edge computing has promoted immense interests in compacting a neural network without sacrificing much accuracy.
Fastened CROWN: Tightened Neural Network Robustness Certificates
We draw inspiration from such work and further demonstrate the optimality of deterministic CROWN (Zhang et al. 2018) solutions in a given linear programming problem under mild constraints.
MiSC: Mixed Strategies Crowdsourcing
Popular crowdsourcing techniques mostly focus on evaluating workers' labeling quality before adjusting their weights during label aggregation.
POPQORN: Quantifying Robustness of Recurrent Neural Networks
The vulnerability to adversarial attacks has been a critical issue for deep neural networks.
Matrix Product Operator Restricted Boltzmann Machines
This work presents the matrix product operator RBM (MPORBM) that utilizes a tensor network generalization of Mv/TvRBM, preserves input formats in both the visible and hidden layers, and results in higher expressive power.
Deep Compression of Sum-Product Networks on Tensor Networks
Sum-product networks (SPNs) represent an emerging class of neural networks with clear probabilistic semantics and superior inference speed over graphical models.
Fast and Accurate Tensor Completion with Total Variation Regularized Tensor Trains
We propose a new tensor completion method based on tensor trains.
A Support Tensor Train Machine
There has been growing interest in extending traditional vector-based machine learning techniques to their tensor forms.
