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Found 16 papers, 1 papers with code
A Multi-Dimensional, Cross-Domain and Hierarchy-Aware Neural Architecture for ISO-Standard Dialogue Act Tagging
Dialogue Act tagging with the ISO 24617-2 standard is a difficult task that involves multi-label text classification across a diverse set of labels covering semantic, syntactic and pragmatic aspects of dialogue.
Multi Label Text Classification
Multi-Label Text Classification
+1
Attention and Pooling based Sigmoid Colon Segmentation in 3D CT images
Pyramid pooling (PyP) and channel-spatial Squeeze and Excitation (csSE) were also used to improve the model performance.
PhishSim: Aiding Phishing Website Detection with a Feature-Free Tool
This method examines the HTML of webpages and computes their similarity with known phishing websites, in order to classify them.
Fast and Data Efficient Reinforcement Learning from Pixels via Non-Parametric Value Approximation
We present Nonparametric Approximation of Inter-Trace returns (NAIT), a Reinforcement Learning algorithm for discrete action, pixel-based environments that is both highly sample and computation efficient.
Ranked #14 on
Atari Games 100k
on Atari 100k
Retrieval Augmented Classification for Long-Tail Visual Recognition
We introduce Retrieval Augmented Classification (RAC), a generic approach to augmenting standard image classification pipelines with an explicit retrieval module.
Ranked #4 on
Long-tail Learning
on iNaturalist 2018
Organ localisation using supervised and semi supervised approaches combining reinforcement learning with imitation learning
The results of experiments on localisation of the Spleen, Left and Right Kidneys in CT Images using supervised and semi supervised learning (SSL) demonstrate the ability to address data limitations with a much smaller data set and fewer annotations, compared to other state-of-the-art methods.
Can Label-Noise Transition Matrix Help to Improve Sample Selection and Label Correction?
Existing methods for learning with noisy labels can be generally divided into two categories: (1) sample selection and label correction based on the memorization effect of neural networks; (2) loss correction with the transition matrix.
Man versus Machine: AutoML and Human Experts' Role in Phishing Detection
Our paper compares the performances of six well-known, state-of-the-art AutoML frameworks on ten different phishing datasets to see whether AutoML-based models can outperform manually crafted machine learning models.
Eccentric Regularization: Minimizing Hyperspherical Energy without explicit projection
Several regularization methods have recently been introduced which force the latent activations of an autoencoder or deep neural network to conform to either a Gaussian or hyperspherical distribution, or to minimize the implicit rank of the distribution in latent space.
Epigenetic evolution of deep convolutional models
In this study, we build upon a previously proposed neuroevolution framework to evolve deep convolutional models.
Simeon -- Secure Federated Machine Learning Through Iterative Filtering
Federated learning enables a global machine learning model to be trained collaboratively by distributed, mutually non-trusting learning agents who desire to maintain the privacy of their training data and their hardware.
Complexity-based speciation and genotype representation for neuroevolution
This paper introduces a speciation principle for neuroevolution where evolving networks are grouped into species based on the number of hidden neurons, which is indicative of the complexity of the search space.
PhishZip: A New Compression-based Algorithm for Detecting Phishing Websites
PhishZip outperforms the use of best-performing HTML-based features in past studies, with a true positive rate of 80. 04%.
Multi-hop Reading Comprehension via Deep Reinforcement Learning based Document Traversal
To address MH-QA specifically, we propose a Deep Reinforcement Learning based method capable of learning sequential reasoning across large collections of documents so as to pass a query-aware, fixed-size context subset to existing models for answer extraction.
Sparse, guided feature connections in an Abstract Deep Network
The ADN system provides a method for developing a very sparse, deep feature topology, based on observed relationships between features, that is able to find solutions in irregular domains, and initialize a network prior to gradient descent learning.
Bootstrapping from Game Tree Search
We implemented our algorithm in a chess program Meep, using a linear heuristic function.
