14 research outputs found
State Legislative Update
As the use of collaborative law increases, the need for uniform laws to help facilitate this process across state lines grew. In February 2007, the Uniform Law Commission (ULC) began drafting an act to address this need. At the July 2009 meeting, the Uniform Collaborative Law Act (UCLA) was unanimously approved by the Commission and was subsequently submitted to the American Bar Association (ABA) House of Delegates for approval. In March 2010, the house approved the amended act after the ULC made a few small changes per the house\u27s recommendation. Since receiving ABA approval, the UCLA has been passed in eight states, most recently Alabama, and introduced this year in five more
Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting
Keyword spotting (KWS) constitutes a major component of human-technology
interfaces. Maximizing the detection accuracy at a low false alarm (FA) rate,
while minimizing the footprint size, latency and complexity are the goals for
KWS. Towards achieving them, we study Convolutional Recurrent Neural Networks
(CRNNs). Inspired by large-scale state-of-the-art speech recognition systems,
we combine the strengths of convolutional layers and recurrent layers to
exploit local structure and long-range context. We analyze the effect of
architecture parameters, and propose training strategies to improve
performance. With only ~230k parameters, our CRNN model yields acceptably low
latency, and achieves 97.71% accuracy at 0.5 FA/hour for 5 dB signal-to-noise
ratio.Comment: Accepted to Interspeech 201
Context Generation Improves Open Domain Question Answering
Closed-book question answering (QA) requires a model to directly answer an
open-domain question without access to any external knowledge. Prior work on
closed-book QA either directly finetunes or prompts a pretrained language model
(LM) to leverage the stored knowledge. However, they do not fully exploit the
parameterized knowledge. To address this issue, we propose a two-stage,
closed-book QA framework which employs a coarse-to-fine approach to extract
relevant knowledge and answer a question. Our approach first generates a
related context for a given question by prompting a pretrained LM. We then
prompt the same LM for answer prediction using the generated context and the
question. Additionally, to eliminate failure caused by context uncertainty, we
marginalize over generated contexts. Experimental results on three QA
benchmarks show that our method significantly outperforms previous closed-book
QA methods (e.g. exact matching 68.6% vs. 55.3%), and is on par with open-book
methods that exploit external knowledge sources (e.g. 68.6% vs. 68.0%). Our
method is able to better exploit the stored knowledge in pretrained LMs without
adding extra learnable parameters or needing finetuning, and paves the way for
hybrid models that integrate pretrained LMs with external knowledge.Comment: 8 pages; Accepted at EACL202
Accelerating a random forest classifier: multi-core, GP-GPU, or FPGA? Accelerating a random forest classifier: multi-core, GP-GPU, or FPGA?
Abstract-Random forest classification is a well known machine learning technique that generates classifiers in the form of an ensemble ("forest") of decision trees. The classification of an input sample is determined by the majority classification by the ensemble. Traditional random forest classifiers can be highly effective, but classification using a random forest is memory bound and not typically suitable for acceleration using FPGAs or GP-GPUs due to the need to traverse large, possibly irregular decision trees. Recent work at Lawrence Livermore National Laboratory has developed several variants of random forest classifiers, including the Compact Random Forest (CRF), that can generate decision trees more suitable for acceleration than traditional decision trees. Our paper compares and contrasts the effectiveness of FPGAs, GP-GPUs, and multi-core CPUs for accelerating classification using models generated by compact random forest machine learning classifiers. Taking advantage of training algorithms that can produce compact random forests composed of many, small trees rather than fewer, deep trees, we are able to regularize the forest such that the classification of any sample takes a deterministic amount of time. This optimization then allows us to execute the classifier in a pipelined or single-instruction multiple thread (SIMT) fashion. We show that FPGAs provide the highest performance solution, but require a multi-chip / multi-board system to execute even modest sized forests. GP-GPUs offer a more flexible solution with reasonably high performance that scales with forest size. Finally, multi-threading via OpenMP on a shared memory system was the simplest solution and provided near linear performance that scaled with core count, but was still significantly slower than the GP-GPU and FPGA