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Found 14 papers, 7 papers with code
Taxonomy-Structured Domain Adaptation
Domain adaptation aims to mitigate distribution shifts among different domains.
Graph-Relational Domain Adaptation
In this work, we relax such uniform alignment by using a domain graph to encode domain adjacency, e. g., a graph of states in the US with each state as a domain and each edge indicating adjacency, thereby allowing domains to align flexibly based on the graph structure.
Self-Supervised Learning of Appliance Usage
We show that this cross-modal prediction task allows us to detect when a particular appliance is used, and the location of the appliance in the home, all in a self-supervised manner, without any labeled data.
Locally Constant Networks
We show how neural models can be used to realize piece-wise constant functions such as decision trees.
Oblique Decision Trees from Derivatives of ReLU Networks
We show how neural models can be used to realize piece-wise constant functions such as decision trees.
Ranked #1 on
Drug Discovery
on PDBbind
$\ell_1$ Adversarial Robustness Certificates: a Randomized Smoothing Approach
Robustness is an important property to guarantee the security of machine learning models.
Towards Robust, Locally Linear Deep Networks
In this paper, we propose a new learning problem to encourage deep networks to have stable derivatives over larger regions.
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers
Specifically, an $\ell_2$ bounded adversary cannot alter the ensemble prediction generated by an additive isotropic Gaussian noise, where the radius for the adversary depends on both the variance of the distribution as well as the ensemble margin at the point of interest.
ProbGAN: Towards Probabilistic GAN with Theoretical Guarantees
Probabilistic modelling is a principled framework to perform model aggregation, which has been a primary mechanism to combat mode collapse in the context of Generative Adversarial Networks (GAN).
Ranked #22 on
Image Generation
on STL-10
Functional Transparency for Structured Data: a Game-Theoretic Approach
We provide a new approach to training neural models to exhibit transparency in a well-defined, functional manner.
RF-Based Fall Monitoring Using Convolutional Neural Networks
Falls are the top reason for fatal and non-fatal injuries among seniors.
Ranked #2 on
RF-based Pose Estimation
on RF-MMD
Game-Theoretic Interpretability for Temporal Modeling
In contrast, we focus on temporal modeling and the problem of tailoring the predictor, functionally, towards an interpretable family.
MUSE: Modularizing Unsupervised Sense Embeddings
This paper proposes to address the word sense ambiguity issue in an unsupervised manner, where word sense representations are learned along a word sense selection mechanism given contexts.
Toward Implicit Sample Noise Modeling: Deviation-driven Matrix Factorization
Specifically, by modeling and learning the deviation of data, we design a novel matrix factorization model.
