If one policy has greater or equal value than the other, in all states, we might say the policy is better. The policy gradient paper guarantees that locally optimal policies can be found with function approximation. This functional returns either the long-term avg rewards or discounted cumulative rewards from a designated start state. In practice, one would obtain an unbiased estimator for the gradient of this functional w.r.t.

Each node asynchronously trains an unsupervised representation of text. Each trains its own model on its own dataset and learns a representations of language (a projection from raw text to embedding) which their neighbours use as inputs to their own model. As they train, they also validate the representations produced by their neighbours, producing a score using a Fishers information metric. We use distillation to extract knowledge from the peers. The result is a local, transfer capable language model at each node.

Abstract: We present a neural program synthesis approach integrating components which write, execute, and assess code to navigate the search space of possible programs. We equip the search process with an interpreter or a read-eval-print-loop (REPL), which immediately executes partially written programs, exposing their semantics. The REPL addresses a basic challenge of program synthesis: tiny changes in syntax can lead to huge changes in semantics. We train a pair of models, a policy that proposes the new piece of code to write, and a value function that assesses the prospects of the code written so-far. At test time we can combine these models with a Sequential Monte Carlo algorithm.

Abstract: Human pose estimation and action recognition are related tasks since both problems are strongly dependent on the human body representation and analysis. Nonetheless, most recent methods in the literature handle the two problems separately. In this work, we propose a multi-task framework for jointly estimating 2D or 3D human poses from monocular color images and classifying human actions from video sequences. We show that a single architecture can be used to solve both problems in an efficient way and still achieves state-of-the-art or comparable results at each task while running at more than 100 frames per second. The proposed method benefits from high parameters sharing between the two tasks by unifying still images and video clips processing in a single pipeline, allowing the model to be trained with data from different categories simultaneously and in a seamlessly way.

There is a benchmark of desktop and laptop GPU cards for deep learning: AI Benchmark. You can run these tests yourself, see https://pypi.org/project/ai-benchmark/. Take note that some GPUs are good for games but not for deep learning (for games 1660 Ti would be good enough and much, much cheaper, vide this and that). For general benchmarks, I recommend UserBenchmark (my Lenovo Y740 with Nvidia RTX 2080 Max-Q here.) For comparison of different cards between frameworks, see Performance in: Keras or PyTorch as your first deep learning framework (June 2018), based on Comparing Deep Learning Frameworks: A Rosetta Stone Approach.

I am currently having a problem with my thesis project and I would like to ask you for help: I basically need to examine a dataset made of images of cells through a siamese network and evaluate their similarity through a contrastive loss. I have managed to run the code, but the weird thing is that I obtain like 61% of accuracy on the training set (I have to improve it) and 100% of accuracy on the test set after 3 epochs and I have no idea why. About my syste, I have divided the data in 80/20, my dataset is made of more or less 115000 images and for my network I have used a margin of 0.2.

The development of artificial intelligence technologies has brought forth the era of autonomous cars, realistic robots, intelligent chatbots, and AI YouTubers. And now, researchers have created an artificial intelligence tool that utilizes language models to identify'fake news'. This tool has been developed to stop the spread of misinformation through stance detection. Based on deep-learning, the AI tool can verify the information provided in posts made on various platforms by comparing it to other posts available on the subject. With the tool, researchers want to eliminate the deceptive posts that have plagued the internet.

We describe the use of Non-Negative Matrix Factorization (NMF) and Latent Dirichlet Allocation (LDA) algorithms to perform topic mining and labelling applied to retail customer communications in attempt to characterize the subject of customers inquiries. In this paper we compare both algorithms in the topic mining performance and propose methods to assign topic subject labels in an automated way.