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Found 19 papers, 16 papers with code
On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis
However, the image reconstruction process within the MRI pipeline, which requires the use of complex hardware and adjustment of a large number of scanner parameters, is highly susceptible to noise of various forms, resulting in arbitrary artifacts within the images.
On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Disease
We hypothesise that the disease classification task can be solved using a very small tailored subset of k-space data, compared to image reconstruction.
Generative Image Translation for Data Augmentation of Bone Lesion Pathology
Insufficient training data and severe class imbalance are often limiting factors when developing machine learning models for the classification of rare diseases.
StarSpace: Embed All The Things!
A framework for training and evaluating AI models on a variety of openly available dialogue datasets.
Training Language Models Using Target-Propagation
While Truncated Back-Propagation through Time (BPTT) is the most popular approach to training Recurrent Neural Networks (RNNs), it suffers from being inherently sequential (making parallelization difficult) and from truncating gradient flow between distant time-steps.
Learning through Dialogue Interactions by Asking Questions
A good dialogue agent should have the ability to interact with users by both responding to questions and by asking questions, and importantly to learn from both types of interaction.
Dialogue Learning With Human-In-The-Loop
An important aspect of developing conversational agents is to give a bot the ability to improve through communicating with humans and to learn from the mistakes that it makes.
Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems
A long-term goal of machine learning is to build intelligent conversational agents.
Sequence Level Training with Recurrent Neural Networks
Many natural language processing applications use language models to generate text.
Ranked #14 on
Machine Translation
on IWSLT2015 German-English
The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations
We introduce a new test of how well language models capture meaning in children's books.
A Neural Attention Model for Abstractive Sentence Summarization
Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build.
Ranked #1 on
Extractive Text Summarization
on DUC 2004 Task 1
Large-scale Simple Question Answering with Memory Networks
Training large-scale question answering systems is complicated because training sources usually cover a small portion of the range of possible questions.
Ranked #1 on
Question Answering
on WebQuestions
(F1 metric)
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
One long-term goal of machine learning research is to produce methods that are applicable to reasoning and natural language, in particular building an intelligent dialogue agent.
Learning Longer Memory in Recurrent Neural Networks
In this paper, we show that learning longer term patterns in real data, such as in natural language, is perfectly possible using gradient descent.
Video (language) modeling: a baseline for generative models of natural videos
We propose a strong baseline model for unsupervised feature learning using video data.
Question Answering with Subgraph Embeddings
Training our system using pairs of questions and structured representations of their answers, and pairs of question paraphrases, yields competitive results on a competitive benchmark of the literature.
Ranked #2 on
Question Answering
on WebQuestions
(F1 metric)
