Quality, diversity, and size of training dataset are critical factors for learning-based gaze estimators.

In zero-shot learning (ZSL), a classifier is trained to recognize visual classes without any image samples. Instead, it is given semantic information about the class, like a textual description or a set of attributes. Learning from attributes could benefit from explicitly modeling structure of the attribute space. Unfortunately, learning of general structure from empirical samples is hard with typical dataset sizes.
Here we describe LAGO, a probabilistic model designed to capture natural soft and-or relations across groups of attributes.

Generalized zero-shot learning (GZSL) is the problem of learning a classifier where some classes have samples and others are learned from side information, like semantic attributes or text description, in a zero-shot learning fashion (ZSL). Training a single model that operates in these two regimes simultaneously is challenging. Here we describe a probabilistic approach that breaks the model into three modular components, and then combines them in a consistent way. Specifically, our model consists of three classifiers: A "gating" model that makes soft decisions if a sample is from a "seen" class, and two experts: a ZSL expert, and an expert model for seen classes.

We address two main difficulties in this approach: How to provide an accurate estimate of the gating probability without any training samples for unseen classes; and how to use expert predictions when it observes samples outside of its domain. The key insight to our approach is to pass information between the three models to improve each one's accuracy, while maintaining the modular structure. We test our approach, adaptive confidence smoothing (COSMO), on four standard GZSL benchmark datasets and find that it largely outperforms state-of-the-art GZSL models. COSMO is also the first model that closes the gap and surpasses the performance of generative models for GZSL, even-though it is a light-weight model that is much easier to train and tune.

Notably, COSMO offers a new view for developing zero-shot models. Thanks to COSMO's modular structure, instead of trying to perform well both on seen and on unseen classes, models can focus on accurate classification of unseen classes, and later consider seen class models.

Capturing the interesting components of an image is a key aspect of image understanding. When a speaker annotates an image, selecting labels that are informative greatly depends on the prior knowledge of a prospective listener. Motivated by cognitive theories of categorization and communication, we present a new unsupervised approach to model this prior knowledge and quantify the informativeness of a description. Specifically, we compute how knowledge of a label reduces uncertainty over the space of labels and utilize this to rank candidate labels for describing an image.

Abstract

Abstract

We address two important concerns in the analysis of the behavior of applications in the presence of hardware errors: (1) when is it important to model how hardware faults lead to erroneous values (instruction-level errors) with high fidelity, as opposed to using simple bit-flipping models, and (2) how to enable fast high-fidelity error injection campaigns, in particular when error detectors are employed.

Single bit-flip has been the most popular error model for resilience studies with fault injection. We use RTL gate-level fault injection to show that this model fails to cover many realistic hardware faults. Specifically, single-event transients from combinational logic and single-event upsets in pipeline latches can lead to complex multi-bit errors at the architecture level. However, although accurate, RTL simulation is too slow to evaluate application-level resilience.

Unified Virtual Memory (UVM) was recently introduced on recent NVIDIA GPUs. Through software and hardware support, UVM provides a coherent shared memory across the entire heterogeneous node, migrating data as appropriate. The older CUDA programming style is akin to older large-memory UNIX applications which used to directly load and unload memory segments. Newer CUDA programs have started taking advantage of UVM for the same reasons of superior programmability that UNIX applications long ago switched to assuming the presence of virtual memory.

Intra-thread instruction duplication offers straightforward and effective pipeline error detection for data-intensive processors. However, software-enforced instruction duplication uses explicit checking instructions, roughly doubles program register usage, and doubles the number of arithmetic operations per thread, potentially leading to severe slowdowns. This paper investigates SwapCodes, a family of software-hardware cooperative mechanisms to accelerate intra-thread duplication in GPUs.