9 research outputs found
Large Language Models of Code Fail at Completing Code with Potential Bugs
Large language models of code (Code-LLMs) have recently brought tremendous
advances to code completion, a fundamental feature of programming assistance
and code intelligence. However, most existing works ignore the possible
presence of bugs in the code context for generation, which are inevitable in
software development. Therefore, we introduce and study the buggy-code
completion problem, inspired by the realistic scenario of real-time code
suggestion where the code context contains potential bugs -- anti-patterns that
can become bugs in the completed program. To systematically study the task, we
introduce two datasets: one with synthetic bugs derived from semantics-altering
operator changes (buggy-HumanEval) and one with realistic bugs derived from
user submissions to coding problems (buggy-FixEval). We find that the presence
of potential bugs significantly degrades the generation performance of the
high-performing Code-LLMs. For instance, the passing rates of CodeGen-2B-mono
on test cases of buggy-HumanEval drop more than 50% given a single potential
bug in the context. Finally, we investigate several post-hoc methods for
mitigating the adverse effect of potential bugs and find that there remains a
large gap in post-mitigation performance.Comment: 25 page
Searching Toward Pareto-Optimal Device-Aware Neural Architectures
Recent breakthroughs in Neural Architectural Search (NAS) have achieved
state-of-the-art performance in many tasks such as image classification and
language understanding. However, most existing works only optimize for model
accuracy and largely ignore other important factors imposed by the underlying
hardware and devices, such as latency and energy, when making inference. In
this paper, we first introduce the problem of NAS and provide a survey on
recent works. Then we deep dive into two recent advancements on extending NAS
into multiple-objective frameworks: MONAS and DPP-Net. Both MONAS and DPP-Net
are capable of optimizing accuracy and other objectives imposed by devices,
searching for neural architectures that can be best deployed on a wide spectrum
of devices: from embedded systems and mobile devices to workstations.
Experimental results are poised to show that architectures found by MONAS and
DPP-Net achieves Pareto optimality w.r.t the given objectives for various
devices.Comment: ICCAD'18 Invited Pape
Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research
Cancer is a complex disease, the understanding and treatment of which are
being aided through increases in the volume of collected data and in the scale
of deployed computing power. Consequently, there is a growing need for the
development of data-driven and, in particular, deep learning methods for
various tasks such as cancer diagnosis, detection, prognosis, and prediction.
Despite recent successes, however, designing high-performing deep learning
models for nonimage and nontext cancer data is a time-consuming,
trial-and-error, manual task that requires both cancer domain and deep learning
expertise. To that end, we develop a reinforcement-learning-based neural
architecture search to automate deep-learning-based predictive model
development for a class of representative cancer data. We develop custom
building blocks that allow domain experts to incorporate the
cancer-data-specific characteristics. We show that our approach discovers deep
neural network architectures that have significantly fewer trainable
parameters, shorter training time, and accuracy similar to or higher than those
of manually designed architectures. We study and demonstrate the scalability of
our approach on up to 1,024 Intel Knights Landing nodes of the Theta
supercomputer at the Argonne Leadership Computing Facility.Comment: SC '19: IEEE/ACM International Conference on High Performance
Computing, Networking, Storage and Analysis, November 17--22, 2019, Denver,
C
SHAPE REPRESENTATION VIA ELEMENTARY SYMMETRIC POLYNOMIALS: A COMPLETE INVARIANT INSPIRED BY THE BISPECTRUM
We address the representation of two-dimensional shape in its most general form, i.e., arbitrary sets of points, that may arise in multiple situations, e.g., sparse sets of specific landmarks, or dense sets of image edge points. Our goal are recognition tasks, where the key is balancing two contradicting demands: shapes that differ by rigid transformations or point re-labeling should have the same representation (invariance) but geometrically distinct shapes should have different representations (completeness). In the paper, we introduce a new shape representation that marries properties of the elementary symmetric polynomials and the bispectrum. Like the power spectrum, the bispectrum is insensitive to signal shifts; however, unlike the power spectrum, the bispectrum is complete. The elementary symmetric polynomials are complete and invariant to variable relabeling. We show that the elementary symmetric polynomials of the shape points depend on the shape orientation in a way that enables interpreting them in the frequency domain and building from them a bispectrum. The result is a shape representation that is complete and invariant to rigid transformations and point-relabeling. The paper also reports experiments that illustrate the proved properties
Data on minute DNA quantification on microvolumetric solutions: comparison of mathematical models and effect of some compounds on the DNA quantification accuracy
This article contains data related to the research article entitled “Novel approach for accurate minute DNA quantification on microvolumetric solutions” (Carvalho et al., 2018). The combination of PicoGreen® with a microvolume fluorospectrometer is a popular DNA quantification method due to its high sensitivity and minimal consumption of sample, being commonly used to evaluate the performance of microfluidic devices designed for DNA purification. In this study, the authors present data related with the effect of DNA fragmentation level. The present data article includes the data used on the precision evaluation, in terms of repeatability, of the mathematical models developed to obtain the standards curve for salmon sperm DNA (low molecular weight). In addition, results related with the effect of some compounds on the DNA quantification accuracy using λDNA are presented