Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for utilizing a content recommendation system powering a streaming media publisher channel to enhance an ad creative being shown to the user via awareness or performance campaigns. This method allows the platform to present exploratory personalized in-channel content to the publisher platform users in endemic banners that run on the platform which then correspondingly helps drive user reach. An example embodiment operates by implementing personalized content banners that may act as a hook for channel users opening their streaming device, both active and lapsed, to enter back into the channel.

Abstract: In various examples, a two-dimensional (2D) and three-dimensional (3D) deep neural network (DNN) is implemented to fuse 2D and 3D object detection results for classifying objects. For example, regions of interest (ROIs) and/or bounding shapes corresponding thereto may be determined using one or more region proposal networks (RPNs)—such as an image-based RPN and/or a depth-based RPN. Each ROI may be extended into a frustum in 3D world-space, and a point cloud may be filtered to include only points from within the frustum. The remaining points may be voxelated to generate a volume in 3D world space, and the volume may be applied to a 3D DNN to generate one or more vectors. The one or more vectors, in addition to one or more additional vectors generated using a 2D DNN processing image data, may be applied to a classifier network to generate a classification for an object.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for utilizing a content recommendation system powering a streaming media publisher channel to enhance an ad creative being shown to the user via awareness or performance campaigns. This method allows the platform to present the most relevant hyper-personalized in-channel content to the publisher platform users in endemic banners that run on the platform which then correspondingly helps drive user reach. An example embodiment operates by implementing personalized content banners that may act as a hook for channel users opening their streaming device, both active and lapsed, to enter back into the channel.

Abstract: In various examples, a two-dimensional (2D) and three-dimensional (3D) deep neural network (DNN) is implemented to fuse 2D and 3D object detection results for classifying objects. For example, regions of interest (ROIs) and/or bounding shapes corresponding thereto may be determined using one or more region proposal networks (RPNs)—such as an image-based RPN and/or a depth-based RPN. Each ROI may be extended into a frustum in 3D world-space, and a point cloud may be filtered to include only points from within the frustum. The remaining points may be voxelated to generate a volume in 3D world space, and the volume may be applied to a 3D DNN to generate one or more vectors. The one or more vectors, in addition to one or more additional vectors generated using a 2D DNN processing image data, may be applied to a classifier network to generate a classification for an object.

Abstract: In various examples, a two-dimensional (2D) and three-dimensional (3D) deep neural network (DNN) is implemented to fuse 2D and 3D object detection results for classifying objects. For example, regions of interest (ROIs) and/or bounding shapes corresponding thereto may be determined using one or more region proposal networks (RPNs)—such as an image-based RPN and/or a depth-based RPN. Each ROI may be extended into a frustum in 3D world-space, and a point cloud may be filtered to include only points from within the frustum. The remaining points may be voxelated to generate a volume in 3D world space, and the volume may be applied to a 3D DNN to generate one or more vectors. The one or more vectors, in addition to one or more additional vectors generated using a 2D DNN processing image data, may be applied to a classifier network to generate a classification for an object.

Abstract: In various examples, a two-dimensional (2D) and three-dimensional (3D) deep neural network (DNN) is implemented to fuse 2D and 3D object detection results for classifying objects. For example, regions of interest (ROIs) and/or bounding shapes corresponding thereto may be determined using one or more region proposal networks (RPNs)—such as an image-based RPN and/or a depth-based RPN. Each ROI may be extended into a frustum in 3D world-space, and a point cloud may be filtered to include only points from within the frustum. The remaining points may be voxelated to generate a volume in 3D world space, and the volume may be applied to a 3D DNN to generate one or more vectors. The one or more vectors, in addition to one or more additional vectors generated using a 2D DNN processing image data, may be applied to a classifier network to generate a classification for an object.

Abstract: Processor hardware detects when memory aliasing occurs, and assures proper operation of the code even in the presence of memory aliasing. The processor defines a special store instruction that is different from a regular store instruction. The special store instruction is used in regions of the computer program where memory aliasing may occur. Because the hardware can detect and correct for memory aliasing, this allows a compiler to make optimizations such as register promotion even in regions of the code where memory aliasing may occur.

Abstract: In various examples, a two-dimensional (2D) and three-dimensional (3D) deep neural network (DNN) is implemented to fuse 2D and 3D object detection results for classifying objects. For example, regions of interest (ROIs) and/or bounding shapes corresponding thereto may be determined using one or more region proposal networks (RPNs)—such as an image-based RPN and/or a depth-based RPN. Each ROI may be extended into a frustum in 3D world-space, and a point cloud may be filtered to include only points from within the frustum. The remaining points may be voxelated to generate a volume in 3D world space, and the volume may be applied to a 3D DNN to generate one or more vectors. The one or more vectors, in addition to one or more additional vectors generated using a 2D DNN processing image data, may be applied to a classifier network to generate a classification for an object.

Abstract: Processor hardware detects when memory aliasing occurs, and assures proper operation of the code even in the presence of memory aliasing. Because the hardware can detect and correct for memory aliasing, this allows a compiler to make optimizations such as register promotion even in regions of the code where memory aliasing can occur. The result is code that is more optimized and therefore runs faster.

Abstract: Processor hardware detects when memory aliasing occurs, and assures proper operation of the code even in the presence of memory aliasing. Because the hardware can detect and correct for memory aliasing, this allows a compiler to make optimizations such as register promotion even in regions of the code where memory aliasing can occur. The compiled code is then executed on the processor hardware, which detects memory aliasing at run-time and assures proper operation of the code even when memory aliasing occurs.

Abstract: Processor hardware detects when memory aliasing occurs, and assures proper operation of the code even in the presence of memory aliasing. The processor defines a special store instruction that is different from a regular store instruction. The special store instruction is used in regions of the computer program where memory aliasing may occur. Because the hardware can detect and correct for memory aliasing, this allows a compiler to make optimizations such as register promotion even in regions of the code where memory aliasing may occur.

Abstract: Processor hardware detects when memory aliasing occurs, and assures proper operation of the code even in the presence of memory aliasing. The processor defines a special store instruction that is different from a regular store instruction. The special store instruction is used in regions of the computer program where memory aliasing may occur. Because the hardware can detect and correct for memory aliasing, this allows a compiler to make optimizations such as register promotion even in regions of the code where memory aliasing may occur.

Abstract: Processor hardware detects when memory aliasing occurs, and assures proper operation of the code even in the presence of memory aliasing. Because the hardware can detect and correct for memory aliasing, this allows a compiler to make optimizations such as register promotion even in regions of the code where memory aliasing can occur. The result is code that is more optimized and therefore runs faster.

Abstract: Processor hardware detects when memory aliasing occurs, and assures proper operation of the code even in the presence of memory aliasing. Because the hardware can detect and correct for memory aliasing, this allows a compiler to make optimizations such as register promotion even in regions of the code where memory aliasing can occur. The result is code that is more optimized and therefore runs faster.

Abstract: Processor hardware detects when memory aliasing occurs, and assures proper operation of the code even in the presence of memory aliasing. Because the hardware can detect and correct for memory aliasing, this allows a compiler to make optimizations such as register promotion even in regions of the code where memory aliasing can occur. The result is code that is more optimized and therefore runs faster.

Abstract: Processor hardware detects when memory aliasing occurs, and assures proper operation of the code even in the presence of memory aliasing. Because the hardware can detect and correct for memory aliasing, this allows a compiler to make optimizations such as register promotion even in regions of the code where memory aliasing can occur. The compiled code is then executed on the processor hardware, which detects memory aliasing at run-time and assures proper operation of the code even when memory aliasing occurs.

Abstract: Processor hardware detects when memory aliasing occurs, and assures proper operation of the code even in the presence of memory aliasing. Because the hardware can detect and correct for memory aliasing, this allows a compiler to make optimizations such as register promotion even in regions of the code where memory aliasing can occur. The result is code that is more optimized and therefore runs faster.

Abstract: Processor hardware detects when memory aliasing occurs, and assures proper operation of the code even in the presence of memory aliasing. Because the hardware can detect and correct for memory aliasing, this allows a compiler to make optimizations such as register promotion even in regions of the code where memory aliasing can occur. The compiled code is then executed on the processor hardware, which detects memory aliasing at run-time and assures proper operation of the code even when memory aliasing occurs.

Abstract: Processor hardware detects when memory aliasing occurs, and assures proper operation of the code even in the presence of memory aliasing. The processor defines a special store instruction that is different from a regular store instruction. The special store instruction is used in regions of the computer program where memory aliasing may occur. Because the hardware can detect and correct for memory aliasing, this allows a compiler to make optimizations such as register promotion even in regions of the code where memory aliasing may occur.

Abstract: Processor hardware detects when memory aliasing occurs, and assures proper operation of the code even in the presence of memory aliasing. The processor defines a special store instruction that is different from a regular store instruction. The special store instruction is used in regions of the computer program where memory aliasing may occur. Because the hardware can detect and correct for memory aliasing, this allows a compiler to make optimizations such as register promotion even in regions of the code where memory aliasing may occur.