Search Results for author:
Found 27 papers, 6 papers with code
Shapley-NAS: Discovering Operation Contribution for Neural Architecture Search
Differentiable architecture search (DARTS) acquires the optimal architectures by optimizing the architecture parameters with gradient descent, which significantly reduces the search cost.
AI Enlightens Wireless Communication: A Transformer Backbone for CSI Feedback
This paper is based on the background of the 2nd Wireless Communication Artificial Intelligence (AI) Competition (WAIC) which is hosted by IMT-2020(5G) Promotion Group 5G+AIWork Group, where the framework of the eigenvector-based channel state information (CSI) feedback problem is firstly provided.
Support Vector Machines under Adversarial Label Contamination
Machine learning algorithms are increasingly being applied in security-related tasks such as spam and malware detection, although their security properties against deliberate attacks have not yet been widely understood.
Generalizable Mixed-Precision Quantization via Attribution Rank Preservation
On the contrary, our GMPQ searches the mixed-quantization policy that can be generalized to largescale datasets with only a small amount of data, so that the search cost is significantly reduced without performance degradation.
AI Enlightens Wireless Communication: Analyses, Solutions and Opportunities on CSI Feedback
In this paper, we give a systematic description of the 1st Wireless Communication Artificial Intelligence (AI) Competition (WAIC) which is hosted by IMT-2020(5G) Promotion Group 5G+AI Work Group.
MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis
The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications.
Searching for polarization in signed graphs: a local spectral approach
In this paper we formulate the problem of finding local polarized communities in signed graphs as a locally-biased eigen-problem.
Hybrid Kronecker Product Decomposition and Approximation
As an effective dimension reduction tool, singular value decomposition is often used to analyze high dimensional matrices, which are traditionally assumed to have a low rank matrix approximation.
KoPA: Automated Kronecker Product Approximation
Specifically, we propose to approximate a given matrix by the sum of a few Kronecker products of matrices, which we refer to as the Kronecker product approximation (KoPA).
Matrix Completion using Kronecker Product Approximation
A matrix completion problem is to recover the missing entries in a partially observed matrix.
Asynchronous "Events" are Better For Motion Estimation
As an instance of event-based camera, Dynamic and Active-pixel Vision Sensor (DAVIS) combines a standard camera and an event-based camera.
Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification
In this paper, we develop a suite of algorithms, called Bonsai, which generalizes the notion of label representation in XMC, and partitions the labels in the representation space to learn shallow trees.
Extreme Multi-Label Classification
General Classification
+2
Dual Ask-Answer Network for Machine Reading Comprehension
The task of question answering or question generation aims to infer an answer or a question when given the counterpart based on context.
Convexification of Neural Graph
Traditionally, most complex intelligence architectures are extremely non-convex, which could not be well performed by convex optimization.
Intelligence Graph
However, to construct powerful intelligence systems with various methods, we propose the intelligence graph (short as \textbf{\textit{iGraph}}), which is composed by both of neural and probabilistic graph, under the framework of forward-backward propagation.
NDT: Neual Decision Tree Towards Fully Functioned Neural Graph
Though traditional algorithms could be embedded into neural architectures with the proposed principle of \cite{xiao2017hungarian}, the variables that only occur in the condition of branch could not be updated as a special case.
Hungarian Layer: Logics Empowered Neural Architecture
However, lacking of logic flow (e. g. \textit{if, for, while}), traditional algorithms (e. g. \textit{Hungarian algorithm, A$^*$ searching, decision tress algorithm}) could not be embedded into this paradigm, which limits the theories and applications.
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images of 70, 000 fashion products from 10 categories, with 7, 000 images per category.
SAR: Semantic Analysis for Recommendation
Recommendation system is a common demand in daily life and matrix completion is a widely adopted technique for this task.
KSR: A Semantic Representation of Knowledge Graph within a Novel Unsupervised Paradigm
Since both aspects and categories are semantics-relevant, the collection of categories in each aspect is treated as the semantic representation of this triple.
SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions
To this end, this paper proposes a semantic representation method for knowledge graph \textbf{(KSR)}, which imposes a two-level hierarchical generative process that globally extracts many aspects and then locally assigns a specific category in each aspect for every triple.
From One Point to A Manifold: Knowledge Graph Embedding For Precise Link Prediction
Knowledge graph embedding aims at offering a numerical knowledge representation paradigm by transforming the entities and relations into continuous vector space.
TransA: An Adaptive Approach for Knowledge Graph Embedding
Knowledge representation is a major topic in AI, and many studies attempt to represent entities and relations of knowledge base in a continuous vector space.
TransG : A Generative Mixture Model for Knowledge Graph Embedding
Recently, knowledge graph embedding, which projects symbolic entities and relations into continuous vector space, has become a new, hot topic in artificial intelligence.
Margin-Based Feed-Forward Neural Network Classifiers
Margin-Based Principle has been proposed for a long time, it has been proved that this principle could reduce the structural risk and improve the performance in both theoretical and practical aspects.
Max-Entropy Feed-Forward Clustering Neural Network
Entropy-Based Principle is the principle with which we could estimate the unknown distribution under some limited conditions.
