For example, the MatMul operation of TensorFlow has'MatMul' as As same as the call id stack, Terra manages the loop id stack for the entire program execution. Figure 2: The result of the case assignment algorithm for the given TraceGraph.2 4 In this section, we describe the case assignment algorithm that Terra uses to explicitly insert the Switch-Case operations in the symbolic graph. The algorithm takes a TraceGraph as an input and returns an ordered list of switch-cases. A switch-case 6is a set of (basic block, control edges) where thebasic block is a linear3 chain of nodes, and the5control edges are the edges that point to the basic block. Every non-overlapping linear chain of nodes in the TraceGraph is uniquely assigned to a basic block so that the ordered list of3switch-cases 5can cover every trace in the TraceGraph.

We introduce regularized Frank-Wolfe, a general and effective algorithm for inference and learning of dense conditional random fields (CRFs). The algorithm optimizes a nonconvex continuous relaxation of the CRF inference problem using vanilla Frank-Wolfe with approximate updates, which are equivalent to minimizing a regularized energy function. Our proposed method is a generalization of existing algorithms such as mean field or concave-convex procedure. This perspective not only offers a unified analysis of these algorithms, but also allows an easy way of exploring different variants that potentially yield better performance. We illustrate this in our empirical results on standard semantic segmentation datasets, where several instantiations of our regularized Frank-Wolfe outperform mean field inference, both as a standalone component and as an end-to-end trainable layer in a neural network. We also show that dense CRFs, coupled with our new algorithms, produce significant improvements over strong CNN baselines.

We introduce regularized Frank-Wolfe, a general and effective algorithm for inference and learning of dense conditional random fields (CRFs). The algorithm optimizes a nonconvex continuous relaxation of the CRF inference problem using vanilla Frank-Wolfe with approximate updates, which are equivalent to minimizing a regularized energy function. Our proposed method is a generalization of existing algorithms such as mean field or concave-convex procedure. This perspective not only offers a unified analysis of these algorithms, but also allows an easy way of exploring different variants that potentially yield better performance. We illustrate this in our empirical results on standard semantic segmentation datasets, where several instantiations of our regularized Frank-Wolfe outperform mean field inference, both as a standalone component and as an end-to-end trainable layer in a neural network. We also show that dense CRFs, coupled with our new algorithms, produce significant improvements over strong CNN baselines.

We focus on the problem of generating high-quality, private synthetic glucose1 traces, a task generalizable to many other time series sources. Existing methods for2 time series data synthesis, such as those using Generative Adversarial Networks3 (GANs), are not able to capture the innate characteristics of glucose data and cannot4 provide any formal privacy guarantees without severely degrading the utility of the5 synthetic data. In this paper we present GlucoSynth, a novel privacy-preserving6 GAN framework to generate synthetic glucose traces. The core intuition behind our7 approach is to conserve relationships amongst motifs (glucose events) within the8 traces, in addition to temporal dynamics. Our framework incorporates differential9 privacy mechanisms to provide strong formal privacy guarantees.

Human language learners are exposed to a trickle of informative, context-sensitive language, but a flood of raw sensory data. Through both social language use and internal processes of rehearsal and practice, language learners are able to build high-level, semantic representations that explain their perceptions. Here, we take inspiration from such processes of "inner speech" in humans (Vygotsky, 1934) to better understand the role of intra-agent speech in embodied behaviour. First, we formally pose intra-agent speech as a semi-supervised problem and develop two algorithms that enable visually grounded captioning with little labeled language data. We then experimentally compute scaling curves over different amounts of labeled data and compare the data efficiency against a supervised learning baseline. Finally, we incorporate intra-agent speech into an embodied, mobile manipulator agent operating in a 3D virtual world, and show that with as few as 150 additional image captions, intra-agent speech endows the agent with the ability to manipulate and answer questions about a new object without any related task-directed experience (zero-shot). Taken together, our experiments suggest that modelling intra-agent speech is effective in enabling embodied agents to learn new tasks efficiently and without direct interaction experience.