no code implementations • COLING 2022 • Stefano Mezza, Wayne Wobcke, Alan Blair
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
no code implementations • 25 Sep 2023 • Md Akizur Rahman, Sonit Singh, Kuruparan Shanmugalingam, Sankaran Iyer, Alan Blair, Praveen Ravindran, Arcot Sowmya
Pyramid pooling (PyP) and channel-spatial Squeeze and Excitation (csSE) were also used to improve the model performance.
no code implementations • 13 Jul 2022 • Rizka Purwanto, Arindam Pal, Alan Blair, Sanjay Jha
This method examines the HTML of webpages and computes their similarity with known phishing websites, in order to classify them.
no code implementations • 7 Mar 2022 • Alexander Long, Alan Blair, Herke van Hoof
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
no code implementations • CVPR 2022 • Alexander Long, Wei Yin, Thalaiyasingam Ajanthan, Vu Nguyen, Pulak Purkait, Ravi Garg, Alan Blair, Chunhua Shen, Anton Van Den Hengel
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
no code implementations • 6 Dec 2021 • Sankaran Iyer, Alan Blair, Laughlin Dawes, Daniel Moses, Christopher White, Arcot Sowmya
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.
no code implementations • 29 Sep 2021 • Yu Yao, Xuefeng Li, Tongliang Liu, Alan Blair, Mingming Gong, Bo Han, Gang Niu, Masashi Sugiyama
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.
no code implementations • 27 Aug 2021 • Rizka Purwanto, Arindam Pal, Alan Blair, Sanjay Jha
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.
no code implementations • 23 Apr 2021 • Xuefeng Li, Alan Blair
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.
1 code implementation • 12 Apr 2021 • Alexander Hadjiivanov, Alan Blair
In this study, we build upon a previously proposed neuroevolution framework to evolve deep convolutional models.
no code implementations • 13 Mar 2021 • Nicholas Malecki, Hye-Young Paik, Aleksandar Ignjatovic, Alan Blair, Elisa Bertino
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.
no code implementations • 11 Oct 2020 • Alexander Hadjiivanov, Alan Blair
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.
no code implementations • 22 Jul 2020 • Rizka Purwanto, Arindam Pal, Alan Blair, Sanjay Jha
PhishZip outperforms the use of best-performing HTML-based features in past studies, with a true positive rate of 80. 04%.
no code implementations • 23 May 2019 • Alex Long, Joel Mason, Alan Blair, Wei Wang
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.
no code implementations • 16 Dec 2014 • Anthony Knittel, Alan Blair
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.
no code implementations • NeurIPS 2009 • Joel Veness, David Silver, Alan Blair, William Uther
We implemented our algorithm in a chess program Meep, using a linear heuristic function.