Intel Corporation

Tahoe Research, Ltd.

Itamar Ben-Ari

Amit Bleiweiss

Jeremie Dreyfuss

Tomer Bar-On

Yaniv Fais

Lev Faivishevsky

Jacob Subag

Michael Behar

Gal Leibovich

Guy Jacob

Amitai Armon

Ehud Cohen

Tomer Schwartz

Uzi Sarel

Yaron Yanai

Galina Ryvchin

Yahav Shadmiy

Ofir Levy

Eyal Yaacoby

Chen Paz

Justin E Gottschlich

Gershom Kutliroff

Niranjan Hasabnis

Dagan Eshar

Oren David Kimhi

Techniques are provided for generation of synthetic 3-dimensional object image variations for training of recognition systems. An example system may include an image synthesizing circuit configured to synthesize a 3D image of the object (including color and depth image pairs) based on a 3D model. The system may also include a background scene generator circuit configured to generate a background for each of the rendered image variations. The system may further include an image pose adjustment circuit configured to adjust the orientation and translation of the object for each of the variations. The system may further include an illumination and visual effect adjustment circuit configured to adjust illumination of the object and the background for each of the variations, and to further adjust visual effects of the object and the background for each of the variations based on application of simulated camera parameters.

Generation of synthetic 3-dimensional object images for recognition systems

Methods, apparatus, systems, and articles of manufacture are disclosed to detect code defects. An example apparatus includes repository interface circuitry to retrieve code repositories corresponding to a programming language of interest, tree generating circuitry to generate parse trees corresponding to code blocks contained in the code repositories, directed acyclic graph (DAG) circuitry to generate DAGs corresponding to respective ones of the parse trees, the DAGs including control flow information and data flow information, abstraction generating circuitry to abstract the DAGs, invariant identification circuitry to extract invariants from the abstracted DAGs, and DAG comparison circuitry to cluster respective ones of the extracted invariants to identify respective ones of the abstracted DAGs with common invariants.

Methods, systems, articles of manufacture and apparatus to detect code defects

In an example, an apparatus comprises a plurality of execution units and logic, at least partially including hardware logic, to gate at least one of a multiply unit or an accumulate unit in response to an input of value zero. Other embodiments are also disclosed and claimed.

Multiply-accumulate '0' data gating

In an example, an apparatus comprises a plurality of execution units; and logic, at least partially including hardware logic, to determine a sub-graph of a network that can be executed in a frequency domain and apply computations in the sub-graph in the frequency domain. Other embodiments are also disclosed and claimed.

Sub-graph in frequency domain and dynamic selection of convolution implementation on a GPU

A system and method for creating a gesture and generating a classifier that can identify the gesture for use with an application is described. The designer constructs a training set of data containing positive and negative examples of the gesture. Machine learning algorithms are used to compute the optimal classification of the training data into positive and negative instances of the gesture. The machine learning algorithms generate a classifier which, given input data, makes a decision on whether the gesture was performed in the input data or not.

System and method for automatically defining and identifying a gesture

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Givat HaShlosha, Israel

Itamar Ben-Ari