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Found 39 papers, 9 papers with code
Inferring Gene Regulatory Neural Networks for Bacterial Decision Making in Biofilms
These incoming external signals are then processed using a Gene Regulatory Network (GRN), exhibiting similarities to modern computing algorithms.
Quantitative Evidence on Overlooked Aspects of Enrollment Speaker Embeddings for Target Speaker Separation
Single channel target speaker separation (TSS) aims at extracting a speaker's voice from a mixture of multiple talkers given an enrollment utterance of that speaker.
DeepSTI: Towards Tensor Reconstruction using Fewer Orientations in Susceptibility Tensor Imaging
Experimental results using both simulation and in vivo human data demonstrate great improvement over state-of-the-art algorithms in terms of the reconstructed tensor image, principal eigenvector maps and tractography results, while allowing for tensor reconstruction with MR phase measured at much less than six different orientations.
A Hierarchical Speaker Representation Framework for One-shot Singing Voice Conversion
It is suspected that a single embedding vector may only capture averaged and coarse-grained speaker characteristics, which is insufficient for the SVC task.
CGAR: Critic Guided Action Redistribution in Reinforcement Leaning
As discussed in this paper, under the settings of the off-policy actor critic algorithms, we demonstrate that the critic can bring more expected discounted rewards than or at least equal to the actor.
Joint learning of object graph and relation graph for visual question answering
Modeling visual question answering(VQA) through scene graphs can significantly improve the reasoning accuracy and interpretability.
Spoofing-Aware Speaker Verification by Multi-Level Fusion
In the second-level fusion, the CM score and ASV scores directly from ASV systems will be concatenated into a prediction block for the final decision.
PathSAGE: Spatial Graph Attention Neural Networks With Random Path Sampling
To address these problems, we propose a model called PathSAGE, which can learn high-order topological information and improve the model's performance by expanding the receptive field.
Characterizing the adversarial vulnerability of speech self-supervised learning
As the paradigm of the self-supervised learning upstream model followed by downstream tasks arouses more attention in the speech community, characterizing the adversarial robustness of such paradigm is of high priority.
Arbitrary Distribution Modeling with Censorship in Real-Time Bidding Advertising
The purpose of Inventory Pricing is to bid the right prices to online ad opportunities, which is crucial for a Demand-Side Platform (DSP) to win advertising auctions in Real-Time Bidding (RTB).
Causal Discovery via Cholesky Factorization
Discovering the causal relationship via recovering the directed acyclic graph (DAG) structure from the observed data is a challenging combinatorial problem.
S$^2$-MLPv2: Improved Spatial-Shift MLP Architecture for Vision
More recently, using smaller patches with a pyramid structure, Vision Permutator (ViP) and Global Filter Network (GFNet) achieve better performance than S$^2$-MLP.
Channel-wise Gated Res2Net: Towards Robust Detection of Synthetic Speech Attacks
This argument motivates the current work that presents a novel, channel-wise gated Res2Net (CG-Res2Net), which modifies Res2Net to enable a channel-wise gating mechanism in the connection between feature groups.
Rethinking Token-Mixing MLP for MLP-based Vision Backbone
By introducing the inductive bias from the image processing, convolution neural network (CNN) has achieved excellent performance in numerous computer vision tasks and has been established as \emph{de facto} backbone.
A Scalable 256-Elements E-Band Phased-Array Transceiver for Broadband Communication
For E-band wireless communications, a high gain steerable antenna with sub-arrays is desired to reduce the implementation complexity.
S$^2$-MLP: Spatial-Shift MLP Architecture for Vision
We discover that the token-mixing MLP is a variant of the depthwise convolution with a global reception field and spatial-specific configuration.
Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning
This work is among the first to perform adversarial defense for ASV without knowing the specific attack algorithms.
Adversarial defense for automatic speaker verification by cascaded self-supervised learning models
Automatic speaker verification (ASV) is one of the core technologies in biometric identification.
Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learning
To obtain an optimal RAN slicing policy for accommodating the spatial-temporal dynamics of vehicle traffic density, we first formulate a constrained RAN slicing problem with the objective to minimize long-term system cost.
Two Types of Mixed Orthogonal Frequency Division Multiplexing (X-OFDM) Waveform for Optical Wireless Communication
For the even sub-carriers in the frequency domain, the signal in the time domain after the IFFT is symmetric.
Replay and Synthetic Speech Detection with Res2net Architecture
This multiple scaling mechanism significantly improves the countermeasure's generalizability to unseen spoofing attacks.
Scalable Adversarial Attack on Graph Neural Networks with Alternating Direction Method of Multipliers
Graph neural networks (GNNs) have achieved high performance in analyzing graph-structured data and have been widely deployed in safety-critical areas, such as finance and autonomous driving.
Learned Proximal Networks for Quantitative Susceptibility Mapping
More importantly, this framework is believed to be the first deep learning QSM approach that can naturally handle an arbitrary number of phase input measurements without the need for any ad-hoc rotation or re-training.
SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization
With the increasing popularity of graph-based learning, Graph Neural Networks (GNNs) win lots of attention from the research and industry field because of their high accuracy.
Investigating Robustness of Adversarial Samples Detection for Automatic Speaker Verification
Orthogonal to prior approaches, this work proposes to defend ASV systems against adversarial attacks with a separate detection network, rather than augmenting adversarial data into ASV training.
Bayesian x-vector: Bayesian Neural Network based x-vector System for Speaker Verification
Our experiment results indicate that the DNN x-vector system could benefit from BNNs especially when the mismatch problem is severe for evaluations using out-of-domain data.
Detection and skeletonization of single neurons and tracer injections using topological methods
Neuroscientific data analysis has traditionally relied on linear algebra and stochastic process theory.
STH: Spatio-Temporal Hybrid Convolution for Efficient Action Recognition
Such a design enables efficient spatio-temporal modeling and maintains a small model scale.
Meta-CoTGAN: A Meta Cooperative Training Paradigm for Improving Adversarial Text Generation
To this end, we introduce a cooperative training paradigm, where a language model is cooperatively trained with the generator and we utilize the language model to efficiently shape the data distribution of the generator against mode collapse.
Deep segmental phonetic posterior-grams based discovery of non-categories in L2 English speech
And then we explore the non-categories by looking for the SPPGs with more than one peak.
FFusionCGAN: An end-to-end fusion method for few-focus images using conditional GAN in cytopathological digital slides
However, although existing image fusion techniques, including traditional algorithms and deep learning-based algorithms, can generate high-quality fused images, they need multiple images with different focus depths in the same field of view.
Adversarial Attacks on GMM i-vector based Speaker Verification Systems
Experiment results show that GMM i-vector systems are seriously vulnerable to adversarial attacks, and the crafted adversarial samples prove to be transferable and pose threats to neuralnetwork speaker embedding based systems (e. g. x-vector systems).
Word embedding re-examined: is the symmetrical factorization optimal?
Such linear transformation will result in these good properties.
End-to-end Deep Reinforcement Learning Based Coreference Resolution
Recent neural network models have significantly advanced the task of coreference resolution.
Ranked #13 on
Coreference Resolution
on CoNLL 2012
Logician: A Unified End-to-End Neural Approach for Open-Domain Information Extraction
In this paper, we consider the problem of open information extraction (OIE) for extracting entity and relation level intermediate structures from sentences in open-domain.
Segmentation of Levator Hiatus Using Multi-Scale Local Region Active contours and Boundary Shape Similarity Constraint
In this paper, a multi-scale framework with local region based active contour and boundary shape similarity constraint is proposed for the segmentation of levator hiatus in ultrasound images.
Global optimization of expensive black-box models based on asynchronous hybrid-criterion with interval reduction
Third, to accelerate the local searching efficiency, searching for a local optimum with sequential quadratic programming (SQP) based on the local surrogate models in the reduced interval, which involves some samples near the current optimum.
Logician and Orator: Learning from the Duality between Language and Knowledge in Open Domain
We propose the task of Open-Domain Information Narration (OIN) as the reverse task of Open Information Extraction (OIE), to implement the dual structure between language and knowledge in the open domain.
Noise Robust IOA/CAS Speech Separation and Recognition System For The Third 'CHIME' Challenge
This paper presents the contribution to the third 'CHiME' speech separation and recognition challenge including both front-end signal processing and back-end speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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