40 citations · 165 across the 28 of their papers we have counts for
5 papers · 1 filter
Comparative Document Summarisation via Classification
Umanga Bista, Alexander Mathews, Minjeong Shin +2
This paper considers extractive summarisation in a comparative setting: given two or more document groups (e.g., separated by publication time), the goal is to select a small numbe…
Complementary-Label Learning for Arbitrary Losses and Models
Takashi Ishida, Gang Niu, Aditya Krishna Menon +1
In contrast to the standard classification paradigm where the true class is given to each training pattern, complementary-label learning only uses training patterns each equipped w…
On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data
Nan Lu, Gang Niu, Aditya Krishna Menon +1
Empirical risk minimization (ERM), with proper loss function and regularization, is the common practice of supervised classification. In this paper, we study training arbitrary (fr…
Monge blunts Bayes: Hardness Results for Adversarial Training
Zac Cranko, Aditya Krishna Menon, Richard Nock +3
The last few years have seen a staggering number of empirical studies of the robustness of neural networks in a model of adversarial perturbations of their inputs. Most rely on an…
Anomaly Detection using One-Class Neural Networks
Raghavendra Chalapathy, Aditya Krishna Menon, Sanjay Chawla
We propose a one-class neural network (OC-NN) model to detect anomalies in complex data sets. OC-NN combines the ability of deep networks to extract a progressively rich representa…