Search Results for author:
Found 24 papers, 9 papers with code
LancBiO: dynamic Lanczos-aided bilevel optimization via Krylov subspace
As a result, the constructed subspace is able to dynamically and incrementally approximate the Hessian inverse vector product with less effort and thus leads to a favorable estimate of the hyper-gradient.
AttentionStore: Cost-effective Attention Reuse across Multi-turn Conversations in Large Language Model Serving
Interacting with humans through multi-turn conversations is a fundamental feature of large language models (LLMs).
Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities
The rise of large foundation models, trained on extensive datasets, is revolutionizing the field of AI.
Segment Anything in Defect Detection
Defect detection plays a crucial role in infrared non-destructive testing systems, offering non-contact, safe, and efficient inspection capabilities.
Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraints
Finally, our experiments demonstrate the promise of our approach to an array of machine-learning problems that involve orthogonality constraints.
UCC: Uncertainty guided Cross-head Co-training for Semi-Supervised Semantic Segmentation
Deep neural networks (DNNs) have witnessed great successes in semantic segmentation, which requires a large number of labeled data for training.
Brachial Plexus Nerve Trunk Segmentation Using Deep Learning: A Comparative Study with Doctors' Manual Segmentation
With the help of deep learning methods, the automatic identification or segmentation of nerves can be realized, assisting doctors in completing nerve block anesthesia accurately and efficiently.
EAGAN: Efficient Two-stage Evolutionary Architecture Search for GANs
Some recent works try to search both generator (G) and discriminator (D), but they suffer from the instability of GAN training.
Ranked #10 on
Image Generation
on STL-10
Edge AI without Compromise: Efficient, Versatile and Accurate Neurocomputing in Resistive Random-Access Memory
Realizing today's cloud-level artificial intelligence functionalities directly on devices distributed at the edge of the internet calls for edge hardware capable of processing multiple modalities of sensory data (e. g. video, audio) at unprecedented energy-efficiency.
New Riemannian preconditioned algorithms for tensor completion via polyadic decomposition
We propose new Riemannian preconditioned algorithms for low-rank tensor completion via the polyadic decomposition of a tensor.
Symplectic eigenvalue problem via trace minimization and Riemannian optimization
We address the problem of computing the smallest symplectic eigenvalues and the corresponding eigenvectors of symmetric positive-definite matrices in the sense of Williamson's theorem.
Optimization and Control Spectral Theory 15A15, 15A18, 70G45
C3-SemiSeg: Contrastive Semi-Supervised Segmentation via Cross-Set Learning and Dynamic Class-Balancing
The semi-supervised semantic segmentation methods utilize the unlabeled data to increase the feature discriminative ability to alleviate the burden of the annotated data.
Alternating minimization algorithms for graph regularized tensor completion
The usage of graph regularization entails benefits in the learning accuracy of LRTC, but at the same time, induces coupling graph Laplacian terms that hinder the optimization of the tensor completion model.
Riemannian Optimization on the Symplectic Stiefel Manifold
The symplectic Stiefel manifold, denoted by $\mathrm{Sp}(2p, 2n)$, is the set of linear symplectic maps between the standard symplectic spaces $\mathbb{R}^{2p}$ and $\mathbb{R}^{2n}$.
Optimization and Control Dynamical Systems
A Training Scheme for the Uncertain Neuromorphic Computing Chips
In this work, we proposed the uncertainty adaptation training scheme (UATS) that tells the uncertainty to the neural network in the training process.
Parallelizable Algorithms for Optimization Problems with Orthogonality Constraints
Numerical experiments in serial illustrate that the novel updating rule for the Lagrangian multipliers significantly accelerates the convergence of PLAM and makes it comparable with the existent feasible solvers for optimization problems with orthogonality constraints, and the performance of PCAL does not highly rely on the choice of the penalty parameter.
Optimization and Control 15A18, 65F15, 65K05, 90C06
Sentence Level Recurrent Topic Model: Letting Topics Speak for Themselves
We propose Sentence Level Recurrent Topic Model (SLRTM), a new topic model that assumes the generation of each word within a sentence to depend on both the topic of the sentence and the whole history of its preceding words in the sentence.
Solving Verbal Comprehension Questions in IQ Test by Knowledge-Powered Word Embedding
Second, we obtain distributed representations of words and relations by leveraging a novel word embedding method that considers the multi-sense nature of words and the relational knowledge among words (or their senses) contained in dictionaries.
Learning Better Word Embedding by Asymmetric Low-Rank Projection of Knowledge Graph
Although these works have achieved certain success, they have neglected some important facts about knowledge graphs: (i) many relationships in knowledge graphs are \emph{many-to-one}, \emph{one-to-many} or even \emph{many-to-many}, rather than simply \emph{one-to-one}; (ii) most head entities and tail entities in knowledge graphs come from very different semantic spaces.
KNET: A General Framework for Learning Word Embedding using Morphological Knowledge
In particular, we introduce a novel neural network architecture called KNET that leverages both contextual information and morphological word similarity built based on morphological knowledge to learn word embeddings.
WordRep: A Benchmark for Research on Learning Word Representations
In this paper, we describe the details of the WordRep collection and show how to use it in different types of machine learning research related to word embedding.
