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Found 18 papers, 8 papers with code
On Inference Stability for Diffusion Models
To minimize that gap, we propose a novel \textit{sequence-aware} loss that aims to reduce the estimation gap to enhance the sampling quality.
KOPPA: Improving Prompt-based Continual Learning with Key-Query Orthogonal Projection and Prototype-based One-Versus-All
Drawing inspiration from prompt tuning techniques applied to Large Language Models, recent methods based on pre-trained ViT networks have achieved remarkable results in the field of Continual Learning.
Continual Learning with Optimal Transport based Mixture Model
To address this issue, in this work, we first propose an online mixture model learning approach based on nice properties of the mature optimal transport theory (OT-MM).
Face Swapping as A Simple Arithmetic Operation
We propose a novel high-fidelity face swapping method called "Arithmetic Face Swapping" (AFS) that explicitly disentangles the intermediate latent space W+ of a pretrained StyleGAN into the "identity" and "style" subspaces so that a latent code in W+ is the sum of an "identity" code and a "style" code in the corresponding subspaces.
Reducing Catastrophic Forgetting in Neural Networks via Gaussian Mixture Approximation
Our paper studies the continual learning (CL) problems in which data comes in sequence and the trained models are expected to be capable of utilizing existing knowledge to solve new tasks without losing performance on previous ones.
From Implicit to Explicit feedback: A deep neural network for modeling sequential behaviours and long-short term preferences of online users
In addition, we go from the hypothesis that a user's preference at a time is a combination of long-term and short-term interests.
Generalization of GANs and overparameterized models under Lipschitz continuity
This result answers the long mystery of why the popular use of Lipschitz constraint for GANs often leads to great success, empirically without a solid theory.
Structured Dropout Variational Inference for Bayesian Neural Networks
Approximate inference in Bayesian deep networks exhibits a dilemma of how to yield high fidelity posterior approximations while maintaining computational efficiency and scalability.
Generalization and Stability of GANs: A theory and promise from data augmentation
Finally, we show why data augmentation can ensure Lipschitz continuity on both the discriminator and generator.
Bag of biterms modeling for short texts
Furthermore, many applications often face with massive and dynamic short texts, causing various computational challenges to the current batch learning algorithms.
A Graph Convolutional Topic Model for Short and Noisy Text Streams
In this paper, to aim at exploiting a knowledge graph effectively, we propose a novel graph convolutional topic model (GCTM) which integrates graph convolutional networks (GCN) into a topic model and a learning method which learns the networks and the topic model simultaneously for data streams.
Dynamic transformation of prior knowledge into Bayesian models for data streams
We show that some existing approaches can forget any knowledge very fast.
Predictive Coding for Locally-Linear Control
High-dimensional observations and unknown dynamics are major challenges when applying optimal control to many real-world decision making tasks.
Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings
It has been shown that implicit connectives can be exploited to improve the performance of the models for implicit discourse relation recognition (IDRR).
Guaranteed inference in topic models
One of the core problems in statistical models is the estimation of a posterior distribution.
Inference in topic models: sparsity and trade-off
One of the core problems in this field is the posterior inference for individual data instances.
Probable convexity and its application to Correlated Topic Models
Contrary to the existing belief of intractability, we show that this inference problem is concave under certain conditions.
Managing sparsity, time, and quality of inference in topic models
In this article, we introduce a simple framework for inference in probabilistic topic models, denoted by FW.
