Since both libraries use cuDNN under the hood, I would expect the individual operations to be similar in speed. However, TensorFlow (in graph mode) compiles a graph so when you run the actual train loop, you have no python overhead outside of the session.run In PyTorch, you are in Python a lot due to the dynamic graph, so I would expect that to add some overhead. Not to mention the fact that having a static graph means you can graph optimizations like node pruning and ordering operations. But in many benchmarks I see online, PyTorch has no problems keeping up with TensorFlow on GPUs.

Such an agent is typically characterised by: (i) a finite set of states 6 S {s i} that describe all possible situations in the environment; (ii) a finite set of actions A {a j} to change in the environment from one situation to another; (iii) a state transition function T (s,a,s null) that specifies the next state s null for having taken action a in the current state s; (iv) a reward function R (s,a,s null) that specifies a numerical value given to the agent for taking action a in state s and transitioning to state s null; and (v) a policy π: S A that defines a mapping from states to actions [2, 30]. The goal of a reinforcement learning agent is to find an optimal policy by maximising its cumulative discounted reward defined as Q (s,a) max π E[r t γr t 1 γ 2 r t 1 ... s t s,a t a,π ], where function Q represents the maximum sum of rewards r t discounted by factor γ at each time step. While a reinforcement learning agent takes actions with probability Pr ( a s) during training, it selects the best action at test time according to π (s) arg max a A Q (s,a). A deep reinforcement learning agent approximates Q using a multi-layer neural network [31]. The Q function is parameterised as Q(s,a; θ), where θ are the parameters or weights of the neural network (recurrent neural network in our case). Estimating these weights requires a dataset of learning experiences D {e 1,...e N} (also referred to as'experience replay memory'), where every experience is described as a tuple e t ( s t,a t,r t,s t 1). Inducing a Q function consists in applying Q-learning updates over minibatches of experience MB {( s,a,r,s null) U (D)} drawn uniformly at random from the full dataset D . This process is implemented in learning algorithms using Deep Q-Networks (DQN) such as those described in [31, 32, 33], and the following section describes a DQN-based algorithm for human-chatbot interaction.

Training chatbots using the reinforcement learning paradigm is challenging due to high-dimensional states, infinite action spaces and the difficulty in specifying the reward function. We address such problems using clustered actions instead of infinite actions, and a simple but promising reward function based on human-likeness scores derived from human-human dialogue data. We train Deep Reinforcement Learning (DRL) agents using chitchat data in raw text---without any manual annotations. Experimental results using different splits of training data report the following. First, that our agents learn reasonable policies in the environments they get familiarised with, but their performance drops substantially when they are exposed to a test set of unseen dialogues. Second, that the choice of sentence embedding size between 100 and 300 dimensions is not significantly different on test data. Third, that our proposed human-likeness rewards are reasonable for training chatbots as long as they use lengthy dialogue histories of >=10 sentences.

The rise of various clouds, edge computing, machine learning, and software-defined everything makes the world increasingly complicated.

It's possible to incorporate machine–learning (ML) and artificial-intelligence (AI) methodologies at the edge, where many compact, embedded IoT …

As interest in religion wanes around the world, and secularism surges, religious leaders have begun searching for innovative ways to spread their message and connect with potential adherents. In the United States, some churches have relaxed dress codes, revamped worship spaces and churned out sermons that place less emphasis on sin, all while embracing Instagram and refashioning faith as a lifestyle brand. In Japan -- where low fertility rates and an aging population are reducing religious affiliation -- Buddhist priests have been warning for more than a decade that their ancient tradition risks extinction. To reverse course, a Kyoto temple has settled on a new plan for connecting with the masses, one that channels ancient wisdom through the technology of the future. Its name is "Mindar," a mostly aluminum androgynous robotic priest who gives plain-spoken sermons that are designed to stimulate interest in Buddhist teachings, according to the machine's designers. Although the robot preaches, it is not programmed to converse with worshipers, though its sermons are translated to English and Chinese on a nearby wall, creators say.

Here at Reviewed, we make a pretty big fuss about media streaming devices: which ones are the best for which people, and why? While we think the other streamers are great (and in some cases better choices), our readers maintain a lot of loyalty to the Amazon Fire TV Stick. Available at a very affordable $40 price point, the Fire TV Stick (and Fire TV Stick 4K) maintain best-seller status month after month, especially when they receive big discounts during events like Prime Day. If you've been thinking about picking up a media streaming device and you've been wondering which streaming device is the best, you might want to consider the Amazon Fire TV Stick. One of the best reasons to spring for either the Amazon Fire TV Stick or Amazon Fire TV Stick 4K is to use Alexa, Amazon's voice assistant, which is built right into the remote on both of the most recent models. If you're already familiar with Alexa, this is a great way to integrate your TV with that ecosystem to a degree.

Abstract: It is well known that Convolutional Neural Networks (CNNs) have significant redundancy in their filter weights. Various methods have been proposed in the literature to compress trained CNNs. These include techniques like pruning weights, filter quantization and representing filters in terms of a basis functions. Our approach falls in this latter class of strategies, but is distinct in that that we show both compressed learning and representation can be achieved without significant modifications of popular CNN architectures. Specifically, any convolution layer of the CNN is easily replaced by two successive convolution layers: the first is a set of fixed filters (that represent the knowledge space of the entire layer and do not change), which is followed by a layer of one-dimensional filters (that represent the learned knowledge in this space).

I've implemented SVRG (Stochastic Variance Reduction Gradient Descent) optimizer for Keras. The goal is to make this optimizer available in Keras as well, which may be beneficial in the case of RL as some papers claimed it is advantageous over Adam.