Search Results for author:
Found 27 papers, 8 papers with code
HLAT: High-quality Large Language Model Pre-trained on AWS Trainium
In this paper, we showcase HLAT: a 7 billion parameter decoder-only LLM pre-trained using trn1 instances over 1. 8 trillion tokens.
Random Walk on Multiple Networks
To take advantage of rich information in multiple networks and make better inferences on entities, in this study, we propose random walk on multiple networks, RWM.
Temporal Output Discrepancy for Loss Estimation-based Active Learning
Inspired by the fact that the samples with higher loss are usually more informative to the model than the samples with lower loss, in this paper we present a novel deep active learning approach that queries the oracle for data annotation when the unlabeled sample is believed to incorporate high loss.
Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms
This work studies the threats of adversarial attack on multivariate probabilistic forecasting models and viable defense mechanisms.
Semi-Supervised Active Learning with Temporal Output Discrepancy
To lower the cost of data annotation, active learning has been proposed to interactively query an oracle to annotate a small proportion of informative samples in an unlabeled dataset.
Attentive Social Recommendation: Towards User And Item Diversities
Social recommendation system is to predict unobserved user-item rating values by taking advantage of user-user social relation and user-item ratings.
Generating Person Images with Appearance-aware Pose Stylizer
Generation of high-quality person images is challenging, due to the sophisticated entanglements among image factors, e. g., appearance, pose, foreground, background, local details, global structures, etc.
Parameter-Free Style Projection for Arbitrary Style Transfer
This paper further presents a real-time feed-forward model to leverage Style Projection for arbitrary image style transfer, which includes a regularization term for matching the semantics between input contents and stylized outputs.
Ultrafast Photorealistic Style Transfer via Neural Architecture Search
Our method consists of a construction step (C-step) to build a photorealistic stylization network and a pruning step (P-step) for acceleration.
SecureGBM: Secure Multi-Party Gradient Boosting
Our experiments using the real-world data showed that SecureGBM can well secure the communication and computation of LightGBM training and inference procedures for the both parties while only losing less than 3% AUC, using the same number of iterations for gradient boosting, on a wide range of benchmark datasets.
Towards Making Deep Transfer Learning Never Hurt
The empirical results show that the proposed descent direction estimation strategy DTNH can always improve the performance of deep transfer learning tasks based on all above regularizers, even when transferring pre-trained weights from inappropriate networks.
NormLime: A New Feature Importance Metric for Explaining Deep Neural Networks
The problem of explaining deep learning models, and model predictions generally, has attracted intensive interest recently.
Fast Universal Style Transfer for Artistic and Photorealistic Rendering
Given a pair of images as the source of content and the reference of style, existing solutions usually first train an auto-encoder (AE) to reconstruct the image using deep features and then embeds pre-defined style transfer modules into the AE reconstruction procedure to transfer the style of the reconstructed image through modifying the deep features.
AGAN: Towards Automated Design of Generative Adversarial Networks
Recent progress in Generative Adversarial Networks (GANs) has shown promising signs of improving GAN training via architectural change.
On the Noisy Gradient Descent that Generalizes as SGD
The gradient noise of SGD is considered to play a central role in the observed strong generalization abilities of deep learning.
StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks
Finally compared to existing universal style transfer networks for photorealistic rendering such as PhotoWCT that stacks multiple well-trained auto-encoders and WCT transforms in a non-end-to-end manner, the architectures designed by StyleNAS produce better style-transferred images with details preserving, using a tiny number of operators/parameters, and enjoying around 500x inference time speed-up.
SHE2: Stochastic Hamiltonian Exploration and Exploitation for Derivative-Free Optimization
Derivative-free optimization (DFO) using trust region methods is frequently used for machine learning applications, such as (hyper-)parameter optimization without the derivatives of objective functions known.
FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary
FSNet has the same architecture as that of the baseline CNN to be compressed, and each convolution layer of FSNet has the same number of filters from FS as that of the basline CNN in the forward process.
An Empirical Study on Regularization of Deep Neural Networks by Local Rademacher Complexity
In this paper, we investigate the role of Rademacher complexity in improving generalization of DNNs and propose a novel regularizer rooted in Local Rademacher Complexity (LRC).
DELTA: DEep Learning Transfer using Feature Map with Attention for Convolutional Networks
Instead of constraining the weights of neural network, DELTA aims to preserve the outer layer outputs of the target network.
Quasi-potential as an implicit regularizer for the loss function in the stochastic gradient descent
We show in this case that the quasi-potential function is related to the noise covariance structure of SGD via a partial differential equation of Hamilton-Jacobi type.
Instance-based Deep Transfer Learning
It makes use of pre-trained models that are learned from a source domain, and utilizes these models for the tasks in a target domain.
Data Dropout: Optimizing Training Data for Convolutional Neural Networks
In this paper, we demonstrate that deep learning models such as convolutional neural networks may not favor all training samples, and generalization accuracy can be further improved by dropping those unfavorable samples.
Understanding Actors and Evaluating Personae with Gaussian Embeddings
Understanding narrative content has become an increasingly popular topic.
Discriminatory Transfer
We observe standard transfer learning can improve prediction accuracies of target tasks at the cost of lowering their prediction fairness -- a phenomenon we named discriminatory transfer.
On the Unreported-Profile-is-Negative Assumption for Predictive Cheminformatics
In cheminformatics, compound-target binding profiles has been a main source of data for research.
Learning Social Circles in Ego Networks based on Multi-View Social Graphs
Based on extensive automatic and manual experimental evaluations, we deliver two major findings: first, multi-view clustering techniques perform better than common single-view clustering techniques, which only use one view or naively integrate all views for detection, second, the standard multi-view clustering technique is less robust than our modified technique, which selectively transfers information across views based on an assumption that sparse network structures are (potentially) incomplete.
