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Found 34 papers, 14 papers with code
DecentLaM: Decentralized Momentum SGD for Large-batch Deep Training
Experimental results on a variety of computer vision tasks and models demonstrate that DecentLaM promises both efficient and high-quality training.
Exponential Graph is Provably Efficient for Decentralized Deep Training
Experimental results on a variety of tasks and models demonstrate that decentralized (momentum) SGD over exponential graphs promises both fast and high-quality training.
BlueFog: Make Decentralized Algorithms Practical for Optimization and Deep Learning
On mainstream DNN training tasks, BlueFog reaches a much higher throughput and achieves an overall $1. 2\times \sim 1. 8\times$ speedup over Horovod, a state-of-the-art distributed deep learning package based on Ring-Allreduce.
MVControl: Adding Conditional Control to Multi-view Diffusion for Controllable Text-to-3D Generation
Our approach enables the generation of controllable multi-view images and view-consistent 3D content.
Controllable Text-to-3D Generation via Surface-Aligned Gaussian Splatting
Building upon our MVControl architecture, we employ a unique hybrid diffusion guidance method to direct the optimization process.
Rethinking Image Forgery Detection via Contrastive Learning and Unsupervised Clustering
To resolve this dilemma, we propose the FOrensic ContrAstive cLustering (FOCAL) method, a novel, simple yet very effective paradigm based on contrastive learning and unsupervised clustering for the image forgery detection.
Bias Loss for Mobile Neural Networks
In compact CNNs, due to the limited number of parameters, abundant features are unlikely to be obtained, and feature diversity becomes an essential characteristic.
Ranked #816 on
Image Classification
on ImageNet
Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework
Most sentence embedding techniques heavily rely on expensive human-annotated sentence pairs as the supervised signals.
Revisiting Self-Training for Few-Shot Learning of Language Model
In this work, we revisit the self-training technique for language model fine-tuning and present a state-of-the-art prompt-based few-shot learner, SFLM.
Analyzing and Evaluating Faithfulness in Dialogue Summarization
The factual correctness of summaries has the highest priority before practical applications.
DynaEval: Unifying Turn and Dialogue Level Evaluation
Effective evaluation metrics should reflect the dynamics of such interaction.
Dynamic Transformers Provide a False Sense of Efficiency
Despite much success in natural language processing (NLP), pre-trained language models typically lead to a high computational cost during inference.
Progressive Poisoned Data Isolation for Training-time Backdoor Defense
Extensive experiments on multiple benchmark datasets and DNN models, assessed against nine state-of-the-art backdoor attacks, demonstrate the superior performance of our PIPD method for backdoor defense.
A Comprehensive Analysis of the Effectiveness of Large Language Models as Automatic Dialogue Evaluators
Yet, existing works on utilizing LLMs for automatic dialogue evaluation are limited in their scope in terms of the number of meta-evaluation datasets, mode of evaluation, coverage of LLMs, etc.
Automated Generation and Ensemble-Learned Matching of X-ray Absorption Spectra
We report the development of XASdb, a large database of computed reference X-ray absorption spectra (XAS), and a novel Ensemble-Learned Spectra IdEntification (ELSIE) algorithm for the matching of spectra.
Materials Science
FoxNet: A Multi-face Alignment Method
Multi-face alignment aims to identify geometry structures of multiple faces in an image, and its performance is essential for the many practical tasks, such as face recognition, face tracking, and face animation.
Pose Estimation for Ground Robots: On Manifold Representation, Integration, Re-Parameterization, and Optimization
In this paper, we focus on motion estimation dedicated for non-holonomic ground robots, by probabilistically fusing measurements from the wheel odometer and exteroceptive sensors.
Visual-Inertial Localization for Skid-Steering Robots with Kinematic Constraints
While visual localization or SLAM has witnessed great progress in past decades, when deploying it on a mobile robot in practice, few works have explicitly considered the kinematic (or dynamic) constraints of the real robotic system when designing state estimators.
Bra-ket wormholes in gravitationally prepared states
The most promising one also leads to a divergent temperature but by making a projection onto low energy states we find that it has features that look similar to the previous Euclidean case.
High Energy Physics - Theory General Relativity and Quantum Cosmology
Large-Scale Training System for 100-Million Classification at Alibaba
In the last decades, extreme classification has become an essential topic for deep learning.
Learned Gradient Compression for Distributed Deep Learning
In contrast, we advocate that the gradients across the nodes are correlated and propose methods to leverage this inter-node redundancy to improve compression efficiency.
IMU Data Processing For Inertial Aided Navigation: A Recurrent Neural Network Based Approach
In this work, we propose a novel method for performing inertial aided navigation, by using deep neural networks (DNNs).
Accelerating Gossip SGD with Periodic Global Averaging
Communication overhead hinders the scalability of large-scale distributed training.
Investigating the Impact of Pre-trained Language Models on Dialog Evaluation
Yet, the impact of different Pr-LMs on the performance of automatic metrics is not well-understood.
Communicate Then Adapt: An Effective Decentralized Adaptive Method for Deep Training
Decentralized adaptive gradient methods, in which each node averages only with its neighbors, are critical to save communication and wall-clock training time in deep learning tasks.
Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression
We establish a convergence lower bound for algorithms whether using unbiased or contractive compressors in unidirection or bidirection.
Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization
While (Lu and Sa, 2021) have recently provided an optimal rate for non-convex stochastic decentralized optimization with weight matrices defined over linear graphs, the optimal rate with general weight matrices remains unclear.
SEVGGNet-LSTM: a fused deep learning model for ECG classification
This paper presents a fused deep learning algorithm for ECG classification.
A 65nm 8b-Activation 8b-Weight SRAM-Based Charge-Domain Computing-in-Memory Macro Using A Fully-Parallel Analog Adder Network and A Single-ADC Interface
Performing data-intensive tasks in the von Neumann architecture is challenging to achieve both high performance and power efficiency due to the memory wall bottleneck.
Lower Bounds and Accelerated Algorithms in Distributed Stochastic Optimization with Communication Compression
In this paper, we investigate the performance limit of distributed stochastic optimization algorithms employing communication compression.
Enhancing Black-Box Few-Shot Text Classification with Prompt-Based Data Augmentation
To achieve this, we treat the black-box model as a feature extractor and train a classifier with the augmented text data.
Association of stroke lesion distributions with atrial fibrillation detected after stroke
The knowledge of the unique characteristics of the population with atrial fibrillation detected after stroke (AFDAS) enables more ischemic stroke patients to benefit from more aggressive anticoagulation therapy and AF management.
TL-nvSRAM-CIM: Ultra-High-Density Three-Level ReRAM-Assisted Computing-in-nvSRAM with DC-Power Free Restore and Ternary MAC Operations
Accommodating all the weights on-chip for large-scale NNs remains a great challenge for SRAM based computing-in-memory (SRAM-CIM) with limited on-chip capacity.
ET3D: Efficient Text-to-3D Generation via Multi-View Distillation
The main insight is that we exploit the images generated by a large pre-trained text-to-image diffusion model, to supervise the training of a text conditioned 3D generative adversarial network.
