Abstract

Released in 2016, the Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset consisting of questions posed by crowdworkers on a set of Wikipedia articles. The answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. Performance on SQuAD surpassed human performance in 2018, and in response the SQuAD2.0 challenge was released, which combines the 100,000 questions in SQuAD1.1 with over 50,000 new, unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. Solutions to SQuAD2.0 will not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering. The Layer 6 NLP Team developed a model that ranked the 2nd place in the leadership board (as of March 28, 2019).

Abstract

The paper presents a two-stage model to evaluate and advance current state-of-the-art in automated playlist continuation using a large scale dataset released by Spotify.

Automatic playlist continuation is a prominent problem in music recommendation. Significant portion of music consumption is now done online through playlists and playlist-like online radio stations. Manually compiling playlists for consumers is a highly time consuming task that is difficult to do at scale given the diversity of tastes and the large amount of musical content available. Consequently, automated playlist continuation has received increasing attention recently. The 2018 ACM RecSys Challenge is dedicated to evaluating and advancing current state-of-the-art in automated playlist continuation using a large scale dataset released by Spotify. In this paper we present our approach to this challenge.

We use a two-stage model where the first stage is optimized for fast retrieval, and the second stage re-ranks retrieved candidates maximizing the accuracy at the top of the recommended list. Our team vl6 achieved 1’st place in both main and creative tracks out of over 100 teams.

Abstract

Pneumonia accounts for over 15% of all deaths of children under 5 years old internationally. In 2015, 920,000 children under the age of 5 died from the disease. In this challenge, we were challenged to build an algorithm to detect a visual signal for pneumonia in medical images. Layer 6 collaborated with 16Bit and developed an ensemble of 15 state-of-the-art object detection models (10 Mask RCNN, 3 YOLOv3, and 2 Faster RCNN models), in combination with a classifier (DenseNet-121architecture pre-trained on NIH Chest X-rays data set) that served to reduce false positives, to detect pneumonia chest X-rays. We found that using a relaxed detection threshold for object detection, whilst requiring unanimous agreement among the detectors, effectively consolidated the need to minimize both false positives and false negatives. Adaptive histogram equalization was used to improve image contrast as a data preprocessing step. We used age, sex, and view position as inputs into the penultimate layer of the classifier to improve performance.

Abstract

Retrieve all the images depicting the same landmark regardless of visual similarity.

Put images with the same landmark closer to the approximated centers of the landmark clusters iteratively.

• Involve local descriptors and geometric verification.
• Test images can be used as additional bridging.

Abstract

In this paper we address the cold start problem in recommender system by providing a standardized framework to benchmark cold start models.

Cold start remains a prominent problem in recommender systems. While rich content information is often available for both users and items few existing models can fully exploit it for personalization. Slow progress in this area can be partially attributed to the lack of publicly available benchmarks to validate and compare models. This year’s ACM Recommender Systems Challenge’17 aimed to address this gap by providing a standardized framework to benchmark cold start models. The challenge organizer XING released a large scaled data collection of user-job interactions from their career oriented social network. Unlike other competitions, here the participating teams were evaluated in two phases – offline and online. Models were first evaluated on the held-out offline test set. Top models were then A/B tested in the online phase where new target users and items were released daily and recommendations were pushed into XING’s live production system. In this paper we present our approach to this challenge, we used a combination of content and neighbor-based models winning both offline and online phases. Our model produced the most consistent online performance wining four of the five online weeks, and showed excellent generalization in the live A/B setting.