We explore fixed-horizon temporal difference (TD) methods, reinforcement learning algorithms for a new kind of value function that predicts the sum of rewards over a $\textit{fixed}$ number of future time steps. To learn the value function for horizon $h$, these algorithms bootstrap from the value function for horizon $h-1$, or some shorter horizon. Because no value function bootstraps from itself, fixed-horizon methods are immune to the stability problems that plague other off-policy TD methods using function approximation (also known as "the deadly triad"). Although fixed-horizon methods require the storage of additional value functions, this gives the agent additional predictive power, while the added complexity can be substantially reduced via parallel updates, shared weights, and $n$-step bootstrapping. We show how to use fixed-horizon value functions to solve reinforcement learning problems competitively with methods such as Q-learning that learn conventional value functions. We also prove convergence of fixed-horizon temporal difference methods with linear and general function approximation. Taken together, our results establish fixed-horizon TD methods as a viable new way of avoiding the stability problems of the deadly triad.

In a very recent work, kernelized locality sensitive hashcodes based representation learning approach has been proposed that has shown to be the most successful in terms of accuracy and computational efficiency for the task (Garg et al., 2019). The model parameters, shared between all the hash functions, are optimized in a supervised manner, whereas an individual hash function is constructed in a randomized fashion. The authors suggest to obtain thousands of (randomized) semantic features extracted from natural language data points into binary hashcodes, and then making classification decision as per the features using hundreds of decision trees, which is the core of their robust classification approach. Even if we extract thousands of semantic features using the hashing approach, it is difficult to ensure that the features extracted from training data points would generalize to a test set. While the inherent randomness in constructarXiv:1909.03881v1 [cs.LG] 9 Sep 2019 Figure 1: On the left, we show an abstract meaning representation (AMR) of a sentence. As per the semantics of the sentence, there is a valid biomedical relationship between the two proteins, Ras and Raf, i.e. Ras catalyzes phosphorylation of Raf; the relation corresponds to a subgraph extracted from the AMR. On the other hand, one of the many invalid biomedical relationships that one could infer is, Ras catalyzes activation of Raf, for which we show the corresponding subgraph too. A given candidate relation automatically hypothesized from the sentence, is binary classified, as valid or invalid, using the subgraph as features.

In natural language understanding components, detecting out-of-domain (OOD) inputs is important for dialogue systems since wrongly accepting these OOD utterances that are not currently supported may lead to catastrophic failures of the entire system. Entropy regularization is an effective solution to avoid such failures, however, its computation heavily depends on OOD data, which are expensive to collect. In this paper, we propose a novel text generation model to produce high-quality OOD samples and thereby improve the performance of OOD detection. The proposed model can also utilize a set of unlabeled data to improve the effectiveness of these generated OOD samples. Experiments show that our method can effectively improve the OOD detection performance of a NLU module. 1 Introduction Natural Language Understanding (NLU) in dialog systems, particularly including task-oriented dialog systems and intelligent personal assistants, is vital for understanding users' inputs and making effective interactions. NLU maps text inputs to structured user intents, and decides the downstream processing pipelines of a dialog system, thereby becoming a precursor for the success of such systems. Recently, various deep neural network (DNN) based NLU models have been proposed and applied in real-world applications (Kim et al., 2018; Sarikaya, 2017; Y oo et al., 2018). Most existing DNN based NLU modules are built by following a closed-world assumption (Fei and Liu, 2016), i.e, the data used in the training and test phrase are drawn from the same distribution. However, such an assumption is commonly violated in practical systems that are deployed in a dynamic or open environment. Specifically, practical NLU systems often encounter o ut-o f-d omain (OOD) inputs that are not supported by the system and thus not observed in the training data.

The problem of verifying whether a textual hypothesis holds the truth based on the given evidence, also known as fact verification, plays an important role in the study of natural language understanding and semantic representation. However, existing studies are mainly restricted to dealing with unstructured evidence (e.g., natural language sentences and documents, news, etc), while verification under structured evidence, such as tables, graphs, and databases, remains unexplored. This paper specifically aims to study the fact verification given semi-structured data as evidence. To this end, we construct a large-scale dataset called \textsc{TabFact} with 16k Wikipedia tables as evidence for 118k human-annotated natural language statements, which are labeled as either {\tt ENTAILED} or {\tt REFUTED}. \textsc{TabFact} is more challenging since it involves both soft linguistic reasoning and hard symbolic reasoning. To address these reasoning challenges, we design two different models: Table-BERT and Latent Program Algorithm (LPA). Table-BERT leverages the state-of-the-art pre-trained language model to encode the linearized tables and statements into continuous vectors for verification. LPA parses statements into LISP-like programs and executes them against the tables to obtain the returned binary value. Both methods achieve similar accuracy but yet far from human performance. We also perform comprehensive analysis and demonstrate great future opportunities. The data and code of the dataset are provided in \url{https://github.com/wenhuchen/Table-Fact-Checking}.

Note: This blog post was authored by Aaron Baughman with Stephen Hammer, Eythan Holladay, Eduardo Morales and Gary Reiss. Tennis play at the US Open consists of 254 matches in the men's and women's singles events totaling tens of thousands of points. During the tournament's two weeks, many matches are played in parallel, and it's virtually impossible for any tennis fan, or the editorial team at the United States Tennis Association (USTA), to capture any sizable percentage of the best points. To help solve this challenge, IBM built an AI system that clips and creates candidate highlight videos and assigns a fair excitement score, all within two minutes of the end of each match. Every highlight is ranked so that tennis fans and video editors at the USTA and its broadcast partners can see the most exciting points of the tournament, while minimizing the influence of player gestures, match analytic score, player rank, player age and crowd size.

Chatbots, virtual assistants, augmented analytic systems typically receive user queries such as "Find me an action movie by Steven Spielberg". The system should correctly detect the intent "find_movie" while filling the slots "genre" with value "action" and "directed_by" with value "Steven Spielberg". This is a Natural Language Understanding (NLU) task kown as Intent Classification & Slot Filling. State-of-the-art performance is typically obtained using recurrent neural network (RNN) based approaches, as well as by leveraging an encoder-decoder architecture with sequence-to-sequence models. In this article we demonstrate hands-on strategies for improving the performance even further by adding Attention mechanism.

Generative Adversarial Networks, or GANs for short, were first described in the 2014 paper by Ian Goodfellow, et al. titled "Generative Adversarial Networks." Since then, GANs have seen a lot of attention given that they are perhaps one of the most effective techniques for generating large, high-quality synthetic images. As such, a number of books have been written about GANs, mostly focusing on how to develop and use the models in practice. In this post, you will discover books written on Generative Adversarial Networks. Discover how to develop DCGANs, conditional GANs, Pix2Pix, CycleGANs, and more with Keras in my new GANs book, with 29 step-by-step tutorials and full source code.

At the VB Transform conference in San Francisco, MissingLink.ai If you're a business, he added, you'd better start investing significantly in AI right now, or another company will outcompete you. Competing with AI Yosi, who served for almost 10 years in the Israeli Defense Forces as a software developer, co-founded MissingLink.ai in 2016 to help software engineers train artificial intelligence to do their jobs faster. Since then, MissingLink has joined the Samsung NEXT product team and launched its platform to help data scientists manage their deep learning operations -- what it calls "DeepOps." Getting a deep learning project off the ground, Yosi told the audience at VB Transform, is relatively easy.

Google Lens was introduced a couple of years ago by Google in a move to spearhead the'AI first' products movement. Now, with the enhancement of machine learning techniques, especially in the domain of image processing and NLP, Google Lens has scaled to new heights. Lens uses computer vision, machine learning and Google's Knowledge Graph to let people turn the things they see in the real world into a visual search box, enabling them to identify objects like plants and animals, or to copy and paste text from the real world into their phone. After Lens in Google Go captures an image, it needs to make sense of the shapes and letters. This is vital for text recognition tasks.

Scientists have long since theorized about machines, capable of simulating human thinking. And following decades of research and development, we have finally reached a point where AI is not only a reality - it's hard to imagine living without it. But what is AI anyway? When we're speaking about modern AI, one of the most popular approaches is called Deep Learning. Inspired by the way biological brains process information, deep learning involves the development of deep neural networks.