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Found 26 papers, 4 papers with code
Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: Joint Gradient Estimation and Tracking
Similarly, for online problems, the proposed method achieves an $\mathcal{O}(m \epsilon^{-3/2})$ sample complexity and an $\mathcal{O}(\epsilon^{-1})$ communication complexity, while the best existing bounds are $\mathcal{O}(m\epsilon^{-2})$ and $\mathcal{O}(\epsilon^{-2})$.
Language Branch Gated Multilingual Neural Machine Translation
Knowledge transfer across languages is crucial for multilingual neural machine translation.
Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets
To the best of our knowledge, we are the first to consider bidder coordination in online repeated auctions with constraints.
A Scalable Neural Network for DSIC Affine Maximizer Auction Design
AMenuNet is always DSIC and individually rational (IR) due to the properties of AMAs, and it enhances scalability by generating candidate allocations through a neural network.
Two-Bit RIS-Aided Communications at 3.5GHz: Some Insights from the Measurement Results Under Multiple Practical Scenes
In particular, we adopt a self-developed broadband intelligent communication system 40MHz-Net (BICT-40N) terminal in order to fully acquire the channel information.
Fine-grained Cross-modal Fusion based Refinement for Text-to-Image Synthesis
The proposed FF-Block integrates an attention block and several convolution layers to effectively fuse the fine-grained word-context features into the corresponding visual features, in which the text information is fully used to refine the initial image with more details.
Score-based Continuous-time Discrete Diffusion Models
Score-based modeling through stochastic differential equations (SDEs) has provided a new perspective on diffusion models, and demonstrated superior performance on continuous data.
Optimal Scaling for Locally Balanced Proposals in Discrete Spaces
Optimal scaling has been well studied for Metropolis-Hastings (M-H) algorithms in continuous spaces, but a similar understanding has been lacking in discrete spaces.
Annealed Training for Combinatorial Optimization on Graphs
However, learning neural networks for CO problems is notoriously difficult in lack of the labeled data as the training is easily trapped at local optima.
Discrete Langevin Sampler via Wasserstein Gradient Flow
It is known that gradient-based MCMC samplers for continuous spaces, such as Langevin Monte Carlo (LMC), can be derived as particle versions of a gradient flow that minimizes KL divergence on a Wasserstein manifold.
To Supervise or Not: How to Effectively Learn Wireless Interference Management Models?
We then provide a series of theoretical results to further understand the properties of the two approaches.
Multi-task Learning of Order-Consistent Causal Graphs
We consider the problem of discovering $K$ related Gaussian directed acyclic graphs (DAGs), where the involved graph structures share a consistent causal order and sparse unions of supports.
CycleFlow: Purify Information Factors by Cycle Loss
SpeechFlow is a powerful factorization model based on information bottleneck (IB), and its effectiveness has been reported by several studies.
Path Auxiliary Proposal for MCMC in Discrete Space
Energy-based Model (EBM) offers a powerful approach for modeling discrete structure, but both inference and learning of EBM are hard as it involves sampling from discrete distributions.
Provable Learning-based Algorithm For Sparse Recovery
In this work, we propose PLISA (Provable Learning-based Iterative Sparse recovery Algorithm) to learn algorithms automatically from data.
Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective
However, it is often challenging for these approaches to learn in a dynamic environment.
Improving Learning to Branch via Reinforcement Learning
Branch-and-Bound~(B\&B) is a general and widely used algorithm paradigm for solving Mixed Integer Programming~(MIP).
Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment
We propose to build the notion of continual learning (CL) into the modeling process of learning wireless systems, so that the learning model can incrementally adapt to the new episodes, {\it without forgetting} knowledge learned from the previous episodes.
Deep generative factorization for speech signal
Various information factors are blended in speech signals, which forms the primary difficulty for most speech information processing tasks.
On the Divergence of Decentralized Non-Convex Optimization
However, by constructing some counter-examples, we show that when certain local Lipschitz conditions (LLC) on the local function gradient $\nabla f_i$'s are not satisfied, most of the existing decentralized algorithms diverge, even if the global Lipschitz condition (GLC) is satisfied, where the sum function $f$ has Lipschitz gradient.
Substituting Gadolinium in Brain MRI Using DeepContrast
Together, these studies validate our hypothesis that a deep learning approach can potentially replace the need for GBCAs in brain MRI.
On Investigation of Unsupervised Speech Factorization Based on Normalization Flow
Speech signals are complex composites of various information, including phonetic content, speaker traits, channel effect, etc.
Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: A Joint Gradient Estimation and Tracking Approach
Similarly, for online problems, the proposed method achieves an $\mathcal{O}(m \epsilon^{-3/2})$ sample complexity and an $\mathcal{O}(\epsilon^{-1})$ communication complexity, while the best existing bounds are $\mathcal{O}(m\epsilon^{-2})$ and $\mathcal{O}(\epsilon^{-2})$, respectively.
Alice's Adventures in the Markovian World
This paper proposes an algorithm Alice having no access to the physics law of the environment, which is actually linear with stochastic noise, and learns to make decisions directly online without a training phase or a stable policy as initial input.
Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms
We show that these algorithms are non-convergent whenever there is some disparity between the expected median and mean over the local gradients.
Locating the boundaries of Pareto fronts: A Many-Objective Evolutionary Algorithm Based on Corner Solution Search
In this paper, an evolutionary many-objective optimization algorithm based on corner solution search (MaOEA-CS) was proposed.
