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Found 25 papers, 7 papers with code
Encoding Argumentation Frameworks to Propositional Logic Systems
This paper enhances the connection between $AF$s and propositional logic systems.
Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable
Machine unlearning is motivated by desire for data autonomy: a person can request to have their data's influence removed from deployed models, and those models should be updated as if they were retrained without the person's data.
Membership Inference Attacks on Diffusion Models via Quantile Regression
Our proposed MI attack learns quantile regression models that predict (a quantile of) the distribution of reconstruction loss on examples not used in training.
Improved Differentially Private Regression via Gradient Boosting
We revisit the problem of differentially private squared error linear regression.
Private Synthetic Data for Multitask Learning and Marginal Queries
A key innovation in our algorithm is the ability to directly handle numerical features, in contrast to a number of related prior approaches which require numerical features to be first converted into {high cardinality} categorical features via {a binning strategy}.
MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation
We integrate these components into a unified framework and present MAMDR, which can be applied to any model structure to perform multi-domain recommendation.
Spectrally Adaptive Common Spatial Patterns
In this work, we propose a novel algorithm called Spectrally Adaptive Common Spatial Patterns (SACSP) that improves CSP by learning a temporal/spectral filter for each spatial filter so that the spatial filters are concentrated on the most relevant temporal frequencies for each user.
Fast Adaptation with Linearized Neural Networks
The inductive biases of trained neural networks are difficult to understand and, consequently, to adapt to new settings.
Deep Transfer Learning with Ridge Regression
The large amount of online data and vast array of computing resources enable current researchers in both industry and academia to employ the power of deep learning with neural networks.
Similarity of Neural Networks with Gradients
A suitable similarity index for comparing learnt neural networks plays an important role in understanding the behaviour of the highly-nonlinear functions, and can provide insights on further theoretical analysis and empirical studies.
Improving Style Transfer with Calibrated Metrics
Style transfer methods produce a transferred image which is a rendering of a content image in the manner of a style image.
Improving Sentence Representations with Multi-view Frameworks
Multi-view learning can provide self-supervision when different views are available of the same data.
A Simple Recurrent Unit with Reduced Tensor Product Representations
idely used recurrent units, including Long-short Term Memory (LSTM) and the Gated Recurrent Unit (GRU), perform well on natural language tasks, but their ability to learn structured representations is still questionable.
Improving Sentence Representations with Consensus Maximisation
Consensus maximisation learning can provide self-supervision when different views are available of the same data.
Exploiting Invertible Decoders for Unsupervised Sentence Representation Learning
The encoder-decoder models for unsupervised sentence representation learning tend to discard the decoder after being trained on a large unlabelled corpus, since only the encoder is needed to map the input sentence into a vector representation.
Multi-view Sentence Representation Learning
Multi-view learning can provide self-supervision when different views are available of the same data.
Quantitative Evaluation of Style Transfer
Style transfer methods produce a transferred image which is a rendering of a content image in the manner of a style image.
Improved Style Transfer by Respecting Inter-layer Correlations
This paper demonstrates that controlling inter-layer correlations yields visible improvements in style transfer methods.
Exploring Asymmetric Encoder-Decoder Structure for Context-based Sentence Representation Learning
Context information plays an important role in human language understanding, and it is also useful for machines to learn vector representations of language.
Speeding up Context-based Sentence Representation Learning with Non-autoregressive Convolutional Decoding
We carefully designed experiments to show that neither an autoregressive decoder nor an RNN decoder is required.
Rethinking Skip-thought: A Neighborhood based Approach
We train our skip-thought neighbor model on a large corpus with continuous sentences, and then evaluate the trained model on 7 tasks, which include semantic relatedness, paraphrase detection, and classification benchmarks.
What Happened to My Dog in That Network: Unraveling Top-down Generators in Convolutional Neural Networks
Top-down information plays a central role in human perception, but plays relatively little role in many current state-of-the-art deep networks, such as Convolutional Neural Networks (CNNs).
