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Found 23 papers, 7 papers with code
Mafin: Enhancing Black-Box Embeddings with Model Augmented Fine-Tuning
Our results demonstrate that Mafin significantly enhances the performance of the black-box embeddings by only requiring the training of a small augmented model.
Active Preference Learning for Large Language Models
A key consideration for aligning these models is how to most effectively use human resources, or model resources in the case where LLMs themselves are used as oracles.
Diffusive Gibbs Sampling
The inadequate mixing of conventional Markov Chain Monte Carlo (MCMC) methods for multi-modal distributions presents a significant challenge in practical applications such as Bayesian inference and molecular dynamics.
Incorporating Neuro-Inspired Adaptability for Continual Learning in Artificial Intelligence
Continual learning aims to empower artificial intelligence (AI) with strong adaptability to the real world.
Constructing Semantics-Aware Adversarial Examples with Probabilistic Perspective
We propose a probabilistic perspective on adversarial examples.
Moment Matching Denoising Gibbs Sampling
Energy-Based Models (EBMs) offer a versatile framework for modeling complex data distributions.
Towards Healing the Blindness of Score Matching
Score-based divergences have been widely used in machine learning and statistics applications.
Integrated Weak Learning
We introduce a label model that can learn to aggregate weak supervision sources differently for different datapoints and takes into consideration the performance of the end-model during training.
Out-of-Distribution Detection with Class Ratio Estimation
In this work, we propose to unify density ratio based methods under a novel framework that builds energy-based models and employs differing base distributions.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Improving VAE-based Representation Learning
Latent variable models like the Variational Auto-Encoder (VAE) are commonly used to learn representations of images.
Generalization Gap in Amortized Inference
The ability of likelihood-based probabilistic models to generalize to unseen data is central to many machine learning applications such as lossless compression.
Parallel Neural Local Lossless Compression
The recently proposed Neural Local Lossless Compression (NeLLoC), which is based on a local autoregressive model, has achieved state-of-the-art (SOTA) out-of-distribution (OOD) generalization performance in the image compression task.
AFEC: Active Forgetting of Negative Transfer in Continual Learning
Without accessing to the old training samples, knowledge transfer from the old tasks to each new task is difficult to determine, which might be either positive or negative.
A Practical PAC-Bayes Generalisation Bound for Deep Learning
Under a PAC-Bayesian framework, we derive an implementation efficient parameterisation invariant metric to measure the difference between our true and empirical risk.
Spread Flows for Manifold Modelling
Flow-based models typically define a latent space with dimensionality identical to the observational space.
On the Out-of-distribution Generalization of Probabilistic Image Modelling
Out-of-distribution (OOD) detection and lossless compression constitute two problems that can be solved by the training of probabilistic models on a first dataset with subsequent likelihood evaluation on a second dataset, where data distributions differ.
Out-of-Distribution Generalization
Out of Distribution (OOD) Detection
Solipsistic Reinforcement Learning
We introduce a new model-based reinforcement learning framework that aims to tackle environments with high dimensional state spaces.
Model-based Reinforcement Learning
reinforcement-learning
+1
On the Latent Space of Flow-based Models
Flow-based generative models typically define a latent space with dimensionality identical to the observational space.
Identifying Informative Latent Variables Learned by GIN via Mutual Information
However, in this paper, we point out that the method taken by GIN for informative latent variables identification is not theoretically supported and can be disproved by experiments.
Wasserstein Robust Reinforcement Learning
Reinforcement learning algorithms, though successful, tend to over-fit to training environments hampering their application to the real-world.
Variational f-divergence Minimization
Probabilistic models are often trained by maximum likelihood, which corresponds to minimizing a specific f-divergence between the model and data distribution.
Spread Divergence
For distributions $\mathbb{P}$ and $\mathbb{Q}$ with different supports or undefined densities, the divergence $\textrm{D}(\mathbb{P}||\mathbb{Q})$ may not exist.
Training generative latent models by variational f-divergence minimization
Probabilistic models are often trained by maximum likelihood, which corresponds to minimizing a specific form of f-divergence between the model and data distribution.
