Search Results for author:
Found 18 papers, 3 papers with code
Distributed Random Reshuffling Methods with Improved Convergence
Notably, both results are comparable to the convergence rates of centralized RR methods (up to constant factors depending on the network topology) and outperform those of previous distributed random reshuffling algorithms.
Text-to-Image Diffusion Models can be Easily Backdoored through Multimodal Data Poisoning
To gain a better understanding of the training process and potential risks of text-to-image synthesis, we perform a systematic investigation of backdoor attack on text-to-image diffusion models and propose BadT2I, a general multimodal backdoor attack framework that tampers with image synthesis in diverse semantic levels.
One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale
Inspired by the unified view, UniDiffuser learns all distributions simultaneously with a minimal modification to the original diffusion model -- perturbs data in all modalities instead of a single modality, inputs individual timesteps in different modalities, and predicts the noise of all modalities instead of a single modality.
Distributed Stochastic Optimization under a General Variance Condition
Distributed stochastic optimization has drawn great attention recently due to its effectiveness in solving large-scale machine learning problems.
CEDAS: A Compressed Decentralized Stochastic Gradient Method with Improved Convergence
In this paper, we consider solving the distributed optimization problem over a multi-agent network under the communication restricted setting.
Alignment-Uniformity aware Representation Learning for Zero-shot Video Classification
Further, we synthesize features of unseen classes by proposing a class generator that interpolates and extrapolates the features of seen classes.
Ranked #14 on
Zero-Shot Action Recognition
on UCF101
Distributed Random Reshuffling over Networks
We show that D-RR inherits favorable characteristics of RR for both smooth strongly convex and smooth nonconvex objective functions.
Provably Accelerated Decentralized Gradient Method Over Unbalanced Directed Graphs
We consider the decentralized optimization problem, where a network of $n$ agents aims to collaboratively minimize the average of their individual smooth and convex objective functions through peer-to-peer communication in a directed graph.
Compressed Gradient Tracking for Decentralized Optimization Over General Directed Networks
The second algorithm is a broadcast-like version of CPP (B-CPP), and it also achieves linear convergence rate under the same conditions on the objective functions.
Improving the Transient Times for Distributed Stochastic Gradient Methods
To the best of our knowledge, EDAS achieves the shortest transient time when the average of the $n$ cost functions is strongly convex and each cost function is smooth.
Multimodal Topic Learning for Video Recommendation
Existing video recommendation systems directly exploit features from different modalities (e. g., user personal data, user behavior data, video titles, video tags, and visual contents) to input deep neural networks, while expecting the networks to online mine user-preferred topics implicitly from these features.
A general framework for decentralized optimization with first-order methods
Decentralized optimization to minimize a finite sum of functions over a network of nodes has been a significant focus within control and signal processing research due to its natural relevance to optimal control and signal estimation problems.
Asymptotic Network Independence in Distributed Stochastic Optimization for Machine Learning
We provide a discussion of several recent results which, in certain scenarios, are able to overcome a barrier in distributed stochastic optimization for machine learning.
A Non-Asymptotic Analysis of Network Independence for Distributed Stochastic Gradient Descent
This paper is concerned with minimizing the average of $n$ cost functions over a network, in which agents may communicate and exchange information with their peers in the network.
Deep Attentive Tracking via Reciprocative Learning
Visual attention, derived from cognitive neuroscience, facilitates human perception on the most pertinent subset of the sensory data.
Swarming for Faster Convergence in Stochastic Optimization
We study a distributed framework for stochastic optimization which is inspired by models of collective motion found in nature (e. g., swarming) with mild communication requirements.
Distributed Stochastic Gradient Tracking Methods
In this paper, we study the problem of distributed multi-agent optimization over a network, where each agent possesses a local cost function that is smooth and strongly convex.
A Distributed Stochastic Gradient Tracking Method
In this paper, we study the problem of distributed multi-agent optimization over a network, where each agent possesses a local cost function that is smooth and strongly convex.
Optimization and Control Distributed, Parallel, and Cluster Computing Multiagent Systems
