The availability of large pre-trained models is changing the landscape of Machine Learning research and practice, moving from a training-from-scratch to a fine-tuning paradigm. While in some applications the goal is to "nudge" the pre-trained distribution towards preferred outputs, in others it is to steer it towards a different distribution over the sample space. Two main paradigms have emerged to tackle this challenge: Reward Maximization (RM) and, more recently, Distribution Matching (DM). RM applies standard Reinforcement Learning (RL) techniques, such as Policy Gradients, to gradually increase the reward signal. DM prescribes to first make explicit the target distribution that the model is fine-tuned to approximate. Here we explore the theoretical connections between the two paradigms, and show that methods such as KL-control developed for RM can also be construed as belonging to DM. We further observe that while DM differs from RM, it can suffer from similar training difficulties, such as high gradient variance. We leverage connections between the two paradigms to import the concept of baseline into DM methods. We empirically validate the benefits of adding a baseline on an array of controllable language generation tasks such as constraining topic, sentiment, and gender distributions in texts sampled from a language model. We observe superior performance in terms of constraint satisfaction, stability and sample efficiency.

Given an input sequence (or prefix), modern language models often assign high probabilities to output sequences that are repetitive, incoherent, or irrelevant to the prefix; as such, model-generated text also contains such artifacts. To address these issues we present RankGen, a 1.2B parameter encoder model for English that scores model generations given a prefix. RankGen can be flexibly incorporated as a scoring function in beam search and used to decode from any pretrained language model. We train RankGen using large-scale contrastive learning to map a prefix close to the ground-truth sequence that follows it and far away from two types of negatives: (1) random sequences from the same document as the prefix, and (2) sequences generated from a large language model conditioned on the prefix. Experiments across four different language models (345M-11B parameters) and two domains show that RankGen significantly outperforms decoding algorithms like nucleus, top-k, and typical sampling, as well as contrastive decoding and search, on both automatic metrics (85.0 vs 77.3 MAUVE over nucleus) as well as human evaluations with English writers (74.5% human preference over nucleus sampling). Analysis reveals that RankGen outputs are more relevant to the prefix and improve continuity and coherence compared to baselines. We release our model checkpoints, code, and human preference data with explanations to facilitate future research.

When she launched the Microwave Video Festival, in 1996, Hong Kong artist and independent curator Ellen Pau had to assure her backers at the city's municipal council that she would avoid the most sophisticated media art from other parts of the world. You can tell from the wording of the original press release, in which she was quoted as saying that the festival would bring "simply produced, yet creative" videos that were "relatable" and "inspiring" to local people. "There was definitely a nervousness about new media and using technology in the arts at the Urban Council," she says. "For the festival to go ahead, I felt it was safer to reassure officials that we wouldn't overwhelm the audience with feats of computing and engineering. Do you have questions about the biggest topics and trends from around the world? Get the answers with SCMP Knowledge, our new platform of curated content with explainers, FAQs, analyses and infographics brought to you by our award-winning team.

Meta AI showed its contributions to the challenging area of deep learning with HTPS, a deep learning model that solved several International Math …

Artificial intelligence and cutting-edge data analysis software mean that underwriters can now make predictions about the weather, …

VANTIVA, headquartered in Paris, France and formerly known as Technicolor, is a global technology leader in designing, developing and supplying innovative products and solutions that connect consumers around the world to the content and services they love – whether at home, at work or in other smart spaces. VANTIVA has also earned a solid reputation for optimizing supply chain performance by leveraging its decades-long expertise in high-precision manufacturing, logistics, fulfilment and distribution. With operations throughout the Americas, Asia Pacific and EMEA, VANTIVA is recognized as a strategic partner by leading firms across various vertical industries, including network service providers, software companies and video game creators for over 25 years. Our relationships with the film and entertainment industry goes back over 100 years by providing end-to-end solutions for our clients. VANTIVA is committed to the highest standards of corporate social responsibility and sustainability across all aspects of their operations.

The subject of conversational mining has become of great interest recently due to the explosion of social and other online media. Supplementing this explosion of text is the advancement in pre-trained language models which have helped us to leverage these sources of information. An interesting domain to analyse is conversations in terms of complexity and value. Complexity arises due to the fact that a conversation can be asynchronous and can involve multiple parties. It is also computationally intensive to process. We use unsupervised methods in our work in order to develop a conversational pattern mining technique which does not require time consuming, knowledge demanding and resource intensive labelling exercises. The task of identifying repeating patterns in sequences is well researched in the Bioinformatics field. In our work, we adapt this to the field of Natural Language Processing and make several extensions to a motif detection algorithm. In order to demonstrate the application of the algorithm on a dynamic, real world data set; we extract motifs from an open-source film script data source. We run an exploratory investigation into the types of motifs we are able to mine.

A vast literature shows that the learning-based visual perception model is sensitive to adversarial noises, but few works consider the robustness of robotic perception models under widely-existing camera motion perturbations. To this end, we study the robustness of the visual perception model under camera motion perturbations to investigate the influence of camera motion on robotic perception. Specifically, we propose a motion smoothing technique for arbitrary image classification models, whose robustness under camera motion perturbations could be certified. The proposed robustness certification framework based on camera motion smoothing provides tight and scalable robustness guarantees for visual perception modules so that they are applicable to wide robotic applications. As far as we are aware, this is the first work to provide robustness certification for the deep perception module against camera motions, which improves the trustworthiness of robotic perception. A realistic indoor robotic dataset with a dense point cloud map for the entire room, MetaRoom, is introduced for the challenging certifiable robust perception task. We conduct extensive experiments to validate the certification approach via motion smoothing against camera motion perturbations. Our framework guarantees the certified accuracy of 81.7% against camera translation perturbation along depth direction within -0.1m ~ 0.1m. We also validate the effectiveness of our method on the real-world robot by conducting hardware experiments on the robotic arm with an eye-in-hand camera. The code is available at https://github.com/HanjiangHu/camera-motion-smoothing.

Knowledge Base Question Answering (KBQA) [1] is an attractive service mining and analytics method that has attracted extensive attention from academic and industrial circles in recent years. Given a natural language question, the KBQA system aims to answer the correct target entities from a given knowledge base (KB) [2]. It relies on certain capabilities including capturing rich semantic information to understand natural language questions clearly and seek correct answers in large scale structured knowledge databases accurately. Knowledge Graph Question Answering (KGQA) [3, 4] is a popular research branch of KBQA which uses a knowledge graph (KG) as its knowledge source [2, 5] and uses factoid triples stored in KG to answer natural language questions. Thanks to KG's unique data structure and its efficient querying capability, users can benefit from a more efficient acquisition of the substantial and valuable KG knowledge, and gain excellent customer experience.

Machine Learning has become the bedrock of recent advances in text, image, video, and audio processing and generation. Most production systems deal with several models during deployment and training, each with a variety of tuned hyperparameters. Furthermore, data collection and processing aspects of ML pipelines are receiving increasing interest due to their importance in creating sustainable high-quality classifiers. We present EdnaML, a framework with a declarative API for reproducible deep learning. EdnaML provides low-level building blocks that can be composed manually, as well as a high-level pipeline orchestration API to automate data collection, data processing, classifier training, classifier deployment, and model monitoring. Our layered API allows users to manage ML pipelines at high-level component abstractions, while providing flexibility to modify any part of it through the building blocks. We present several examples of ML pipelines with EdnaML, including a large-scale fake news labeling and classification system with six sub-pipelines managed by EdnaML.