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Found 23 papers, 6 papers with code
Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data
Deep semi-supervised learning (SSL) has been shown very effectively.
Learning with Feature and Distribution Evolvable Streams
Besides the feature space evolving, it is noteworthy that the data distribution often changes in streaming data.
Learning to Solve Routing Problems via Distributionally Robust Optimization
Recent deep models for solving routing problems always assume a single distribution of nodes for training, which severely impairs their cross-distribution generalization ability.
Neural Grapheme-to-Phoneme Conversion with Pre-trained Grapheme Models
Neural network models have achieved state-of-the-art performance on grapheme-to-phoneme (G2P) conversion.
Fast Abductive Learning by Similarity-based Consistency Optimization
To utilize the raw inputs and symbolic knowledge simultaneously, some recent neuro-symbolic learning methods use abduction, i. e., abductive reasoning, to integrate sub-symbolic perception and logical inference.
Theoretical Exploration of Flexible Transmitter Model
Neural network models generally involve two important components, i. e., network architecture and neuron model.
LIFE: Learning Individual Features for Multivariate Time Series Prediction with Missing Values
Multivariate time series (MTS) prediction is ubiquitous in real-world fields, but MTS data often contains missing values.
Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning
In many real-world imitation learning tasks, the demonstrator and the learner have to act under different observation spaces.
Semi-Supervised Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transport
Moreover, we introduce the extrinsic unlabeled multi-modal multi-instance data, and propose the M3DNS, which considers the instance-level auto-encoder for single modality and modified bag-level optimal transport to strengthen the consistency among modalities.
Voice Conversion by Cascading Automatic Speech Recognition and Text-to-Speech Synthesis with Prosody Transfer
In this paper, we present a ASR-TTS method for voice conversion, which used iFLYTEK ASR engine to transcribe the source speech into text and a Transformer TTS model with WaveNet vocoder to synthesize the converted speech from the decoded text.
Provably Robust Metric Learning
Metric learning is an important family of algorithms for classification and similarity search, but the robustness of learned metrics against small adversarial perturbations is less studied.
Soft Gradient Boosting Machine
Gradient Boosting Machine has proven to be one successful function approximator and has been widely used in a variety of areas.
Spanning Attack: Reinforce Black-box Attacks with Unlabeled Data
By constraining adversarial perturbations in a low-dimensional subspace via spanning an auxiliary unlabeled dataset, the spanning attack significantly improves the query efficiency of a wide variety of existing black-box attacks.
Multi-Label Learning with Deep Forest
In multi-label learning, each instance is associated with multiple labels and the crucial task is how to leverage label correlations in building models.
ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech
Spoofing attacks within a logical access (LA) scenario are generated with the latest speech synthesis and voice conversion technologies, including state-of-the-art neural acoustic and waveform model techniques.
Imitation Learning from Pixel-Level Demonstrations by HashReward
One of the key issues for imitation learning lies in making policy learned from limited samples to generalize well in the whole state-action space.
Rectify Heterogeneous Models with Semantic Mapping
On the way to the robust learner for real-world applications, there are still great challenges, including considering unknown environments with limited data.
Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection
This design is biologically-plausible, as it likes a human visual system to compare different possible segmentation solutions to address the ambiguous boundary issue.
What Makes Objects Similar: A Unified Multi-Metric Learning Approach
In UM2L, a type of combination operator is introduced for distance characterization from multiple perspectives, and thus can introduce flexibilities for representing and utilizing both spatial and semantic linkages.
DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images
By observing the relationship between the receptive field sizes of the different layers in the network and the skeleton scales they can capture, we introduce two scale-associated side outputs to each stage of the network.
Shape Recognition by Bag of Skeleton-associated Contour Parts
Contour and skeleton are two complementary representations for shape recognition.
Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs
Object skeleton is a useful cue for object detection, complementary to the object contour, as it provides a structural representation to describe the relationship among object parts.
Multi-Label Active Learning from Crowds
Multi-label active learning is a hot topic in reducing the label cost by optimally choosing the most valuable instance to query its label from an oracle.
