Sparse linear algebra (SLA) operations are fundamental building blocks for many important applications, such as data analytics, graph processing, machine learning, and scientific computing. In particular, four compute kernels in SLA are widely used, including sparse-matrix-dense-vector multiplication, sparse-matrix-dense-matrix multiplication, sparse-matrix-sparse-vector multiplication, and sparse-matrix-sparse-matrix multiplication. Recently, an active area of research has emerged to build specialized hardware accelerators for these SLA kernels. However, existing efforts mostly focus on accelerating a single kernel and the proposed accelerator architectures are often limited to a specific compute pattern, such as inner, outer, or row-wise product. This work proposes Vesper, a high-performance and versatile sparse accelerator that supports all four important SLA kernels while being configurable to execute the compute patterns suitable for different kernels under various degrees of sparsity. To enable rapid exploration of the large architectural design and configuration space, we devise an analytical model to estimate the performance of an SLA kernel running on a given hardware configuration using a specific compute pattern. Guided by our model, we build a flexible yet efficient accelerator architecture that maximizes the resource sharing amongst the hardware modules used for different SLA kernels and the associated compute patterns. We evaluate the performance of Vesper using gem5 on a diverse set of matrices from SuiteSparse. Our experiment results show that Vesper achieves a comparable or higher throughput with increased bandwidth efficiency than the state-of-the-art accelerators that are tailor-made for a specific SLA kernel. In addition, we evaluate Vesper on a real-world application called label propagation (LP), an iterative graph-based learning algorithm that involves multiple SLA kernels and exhibits varying degrees of sparsity across iterations. Compared to CPU- and GPU-based executions, Vesper speeds up the LP algorithm by$12.0\times $and$1.7\times $, respectively.

We tackle the challenge of learning a distribution over complex, realistic, indoor scenes. In this paper, we introduce Generative Scene Networks (GSN), which learns to decompose scenes into a collection of many local radiance fields that can be rendered from a free moving camera. Our model can be used as a prior to generate new scenes, or to complete a scene given only sparse 2D observations. Recent work has shown that generative models of radiance fields can capture properties such as multi-view consistency and view-dependent lighting. However, these models are specialized for constrained viewing of single objects, such as cars or faces. Due to the size and complexity of realistic indoor environments, existing models lack the representational capacity to adequately capture them. Our decomposition scheme scales to larger and more complex scenes while preserving details and diversity, and the learned prior enables high-quality rendering from viewpoints that are significantly different from observed viewpoints. When compared to existing models, GSN produces quantitatively higher-quality scene renderings across several different scene datasets.

Offline Reinforcement Learning promises to learn effective policies from previously-collected, static datasets without the need for exploration. However, existing Q-learning and actor-critic based off-policy RL algorithms fail when bootstrapping from out-of-distribution (OOD) actions or states. We hypothesize that a key missing ingredient from the existing methods is a proper treatment of uncertainty in the offline setting. We propose Uncertainty Weighted Actor-Critic (UWAC), an algorithm that detects OOD state-action pairs and down-weights their contribution in the training objectives accordingly. Implementation-wise, we adopt a practical and effective dropout-based uncertainty estimation method that introduces very little overhead over existing RL algorithms. Empirically, we observe that UWAC substantially improves model stability during training. In addition, UWAC out-performs existing offline RL methods on a variety of competitive tasks, and achieves significant performance gains over the state-of-the-art baseline on datasets with sparse demonstrations collected from human experts.

State-of-the-art learning-based monocular 3D reconstruction methods learn priors over object categories on the training set, and as a result struggle to achieve reasonable generalization to object categories unseen during training. In this paper we study the inductive biases encoded in the model architecture that impact the generalization of learning-based 3D reconstruction methods. We find that 3 inductive biases impact performance: the spatial extent of the encoder, the use of the underlying geometry of the scene to describe point features, and the mechanism to aggregate information from multiple views. Additionally, we propose mechanisms to enforce those inductive biases: a point representation that is aware of camera position, and a variance cost to aggregate information across views. Our model achieves state-of-the-art results on the standard ShapeNet 3D reconstruction benchmark in various settings.

We conduct an in-depth exploration of different strategies for doing event detection in videos using convolutional neural networks (CNNs) trained for image classification. We study different ways of performing spatial and temporal pooling, feature normalization, choice of CNN layers as well as choice of classifiers. Making judicious choices along these dimensions led to a very significant increase in performance over more naive approaches that have been used till now. We evaluate our approach on the challenging TRECVID MED'14 dataset with two popular CNN architectures pretrained on ImageNet. On this MED'14 dataset, our methods, based entirely on image-trained CNN features, can outperform several state-of-the-art non-CNN models. Our proposed late fusion of CNN- and motion-based features can further increase the mean average precision (mAP) on MED'14 from 34.95% to 38.74%. The fusion approach achieves the state-of-the-art classification performance on the challenging UCF-101 dataset.

We use Long Short Term Memory (LSTM) networks to learn representations of video sequences. Our model uses an encoder LSTM to map an input sequence into a fixed length representation. This representation is decoded using single or multiple decoder LSTMs to perform different tasks, such as reconstructing the input sequence, or predicting the future sequence. We experiment with two kinds of input sequences – patches of image pixels and high-level representations (“percepts") of video frames extracted using a pretrained convolutional net. We explore different design choices such as whether the decoder LSTMs should condition on the generated output. We analyze the outputs of the model qualitatively to see how well the model can extrapolate the learned video representation into the future and into the past. We further evaluate the representations by finetuning them for a supervised learning problem – human action recognition on the UCF-101 and HMDB-51 datasets. We show that the representations help improve classification accuracy, especially when there are only few training examples. Even models pretrained on unrelated datasets (300 hours of YouTube videos) can help action recognition performance.

This paper proposes a way of improving classification performance for classes which have very few training examples. The key idea is to discover classes which are similar and transfer knowledge among them. Our method organizes the classes into a tree hierarchy. The tree structure can be used to impose a generative prior over classification parameters. We show that these priors can be combined with discriminative models such as deep neural networks. Our method benefits from the power of discriminative training of deep neural networks, at the same time using tree-based generative priors over classification parameters. We also propose an algorithm for learning the underlying tree structure. This gives the model some flexibility to tune the tree so that the tree is pertinent to task being solved. We show that the model can transfer knowledge across related classes using fixed semantic trees. Moreover, it can learn new meaningful trees usually leading to improved performance. Our method achieves state-of-the-art classification results on the CIFAR-100 image data set and the MIR Flickr multimodal data set.

We propose a Deep Boltzmann Machine for learning a generative model of multimodal data. We show how to use the model to extract a meaningful representation of multimodal data. We find that the learned representation is useful for classification and information retreival tasks, and hence conforms to some notion of semantic similarity. The model defines a probability density over the space of multimodal inputs. By sampling from the conditional distributions over each data modality, it possible to create the representation even when some data modalities are missing. Our experimental results on bi-modal data consisting of images and text show that the Multimodal DBM can learn a good generative model of the joint space of image and text inputs that is useful for information retrieval from both unimodal and multimodal queries. We further demonstrate that our model can significantly outperform SVMs and LDA on discriminative tasks. Finally, we compare our model to other deep learning methods, including autoencoders and deep belief networks, and show that it achieves significant gains.