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Found 14 papers, 5 papers with code
Sparks of Artificial General Intelligence: Early experiments with GPT-4
We contend that (this early version of) GPT-4 is part of a new cohort of LLMs (along with ChatGPT and Google's PaLM for example) that exhibit more general intelligence than previous AI models.
Ranked #32 on
Arithmetic Reasoning
on GSM8K
The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Speech Quality and Testing Framework
In this challenge, we open-source a large clean speech and noise corpus for training the noise suppression models and a representative test set to real-world scenarios consisting of both synthetic and real recordings.
The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results
In this challenge, we open-sourced a large clean speech and noise corpus for training the noise suppression models and a representative test set to real-world scenarios consisting of both synthetic and real recordings.
Sparse Partially Linear Additive Models
Thus, to make a GPLAM a viable approach in situations in which little is known $a~priori$ about the features, one must overcome two primary model selection challenges: deciding which features to include in the model and determining which of these features to treat nonlinearly.
Lumos: A Library for Diagnosing Metric Regressions in Web-Scale Applications
Web-scale applications can ship code on a daily to weekly cadence.
Learning State Representations for Query Optimization with Deep Reinforcement Learning
In the database field, query optimization remains a difficult problem.
A Quantitative Evaluation Framework for Missing Value Imputation Algorithms
We consider the problem of quantitatively evaluating missing value imputation algorithms.
ALEX: An Updatable Adaptive Learned Index
The original work by Kraska et al. shows that a learned index beats a B+Tree by a factor of up to three in search time and by an order of magnitude in memory footprint.
Supervised Classifiers for Audio Impairments with Noisy Labels
The user feedback after the call can act as the ground truth labels for training a supervised classifier on a large audio dataset.
A scalable noisy speech dataset and online subjective test framework
Our subjective MOS evaluation is the first large scale evaluation of Speech Enhancement algorithms that we are aware of.
Reinforcement learning for bandwidth estimation and congestion control in real-time communications
Bandwidth estimation and congestion control for real-time communications (i. e., audio and video conferencing) remains a difficult problem, despite many years of research.
Qd-tree: Learning Data Layouts for Big Data Analytics
For a given workload, however, such techniques are unable to optimize for the important metric of the number of blocks accessed by a query.
Programming by Rewards
The goal of the synthesizer is to synthesize a "decision function" $f$ which transforms the features to a decision value for the black-box component so as to maximize the expected reward $E[r \circ f (x)]$ for executing decisions $f(x)$ for various values of $x$.
Resonance: Replacing Software Constants with Context-Aware Models in Real-time Communication
Large software systems tune hundreds of 'constants' to optimize their runtime performance.
