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Found 12 papers, 7 papers with code
Dispersed EM-VAEs for Interpretable Text Generation
Interpretability is important in text generation for guiding the generation with interpretable attributes.
Side Channel-Assisted Inference Leakage from Machine Learning-based ECG Classification
In particular, we first identify the vulnerability of DTW for ECG classification, i. e., the correlation between warping path choice and prediction results.
Learning Instance-Specific Augmentations by Capturing Local Invariances
We introduce InstaAug, a method for automatically learning input-specific augmentations from data.
On Incorporating Inductive Biases into VAEs
InteL-VAEs use an intermediary set of latent variables to control the stochasticity of the encoding process, before mapping these in turn to the latent representation using a parametric function that encapsulates our desired inductive bias(es).
Do You Have the Right Scissors? Tailoring Pre-trained Language Models via Monte-Carlo Methods
It has been a common approach to pre-train a language model on a large corpus and fine-tune it on task-specific data.
Generating Fluent Adversarial Examples for Natural Languages
Efficiently building an adversarial attacker for natural language processing (NLP) tasks is a real challenge.
Improving Maximum Likelihood Training for Text Generation with Density Ratio Estimation
Auto-regressive sequence generative models trained by Maximum Likelihood Estimation suffer the exposure bias problem in practical finite sample scenarios.
Kernelized Bayesian Softmax for Text Generation
Neural models for text generation require a softmax layer with proper token embeddings during the decoding phase.
Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation
To enhance the controllability and interpretability, one can replace the Gaussian prior with a mixture of Gaussian distributions (GM-VAE), whose mixture components could be related to hidden semantic aspects of data.
CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling
In real-world applications of natural language generation, there are often constraints on the target sentences in addition to fluency and naturalness requirements.
Tree2Tree Learning with Memory Unit
Traditional recurrent neural network (RNN) or convolutional neural net- work (CNN) based sequence-to-sequence model can not handle tree structural data well.
