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Found 11 papers, 5 papers with code
Make Prompts Adaptable: Bayesian Modeling for Vision-Language Prompt Learning with Data-Dependent Prior
This paper presents a Bayesian-based framework of prompt learning, which could alleviate the overfitting issues on few-shot learning application and increase the adaptability of prompts on unseen instances.
Loss-Curvature Matching for Dataset Selection and Condensation
Training neural networks on a large dataset requires substantial computational costs.
Neural Posterior Regularization for Likelihood-Free Inference
A simulation is useful when the phenomenon of interest is either expensive to regenerate or irreproducible with the same context.
Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder
Therefore, this paper proposes Disentangled Causal Effect Variational Autoencoder (DCEVAE) to resolve this limitation by disentangling the exogenous uncertainty into two latent variables: either 1) independent to interventions or 2) correlated to interventions without causality.
Neutralizing Gender Bias in Word Embeddings with Latent Disentanglement and Counterfactual Generation
Recent research demonstrates that word embeddings, trained on the human-generated corpus, have strong gender biases in embedding spaces, and these biases can result in the discriminative results from the various downstream tasks.
Sequential Likelihood-Free Inference with Neural Proposal
This paper introduces a new sampling approach, called Neural Proposal (NP), of the simulation input that resolves the biased data collection as it guarantees the i. i. d.
Adversarial Likelihood-Free Inference on Black-Box Generator
Generative Adversarial Network (GAN) can be viewed as an implicit estimator of a data distribution, and this perspective motivates using the adversarial concept in the true input parameter estimation of black-box generators.
Neutralizing Gender Bias in Word Embedding with Latent Disentanglement and Counterfactual Generation
Recent research demonstrates that word embeddings, trained on the human-generated corpus, have strong gender biases in embedding spaces, and these biases can result in the discriminative results from the various downstream tasks.
Generalized Gumbel-Softmax Gradient Estimator for Generic Discrete Random Variables
Stochastic gradient estimators of discrete random variables are widely explored, for example, Gumbel-Softmax reparameterization trick for Bernoulli and categorical distributions.
Sequential Recommendation with Relation-Aware Kernelized Self-Attention
This work merges the self-attention of the Transformer and the sequential recommendation by adding a probabilistic model of the recommendation task specifics.
Dirichlet Variational Autoencoder
The experimental results show that 1) DirVAE models the latent representation result with the best log-likelihood compared to the baselines; and 2) DirVAE produces more interpretable latent values with no collapsing issues which the baseline models suffer from.
