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Found 34 papers, 11 papers with code
Dynamic indifference pricing via the G-expectation
We study the dynamic indifference pricing with ambiguity preferences.
Signed Support Recovery for Single Index Models in High-Dimensions
When $s=O(p^{1-\delta})$ for some $\delta>0$, we demonstrate that both procedures can succeed in recovering the support of $\boldsymbol{\beta}$ as long as the rescaled sample size $\kappa=\frac{n}{s\log(p-s)}$ is larger than a certain critical threshold.
ForkBase: An Efficient Storage Engine for Blockchain and Forkable Applications
By integrating the core application properties into the storage, ForkBase not only delivers high performance but also reduces development effort.
Databases Cryptography and Security Distributed, Parallel, and Cluster Computing
Attention Regularized Sequence-to-Sequence Learning for E2E NLG Challenge
This paper describes our system used for the end-to-end (E2E) natural language generation (NLG) challenge.
Ranked #8 on
Data-to-Text Generation
on E2E NLG Challenge
Towards Scaling Blockchain Systems via Sharding
In this work, we take a principled approach to apply sharding, which is a well-studied and proven technique to scale out databases, to blockchain systems in order to improve their transaction throughput at scale.
Distributed, Parallel, and Cluster Computing Cryptography and Security Databases
Object-Level Representation Learning for Few-Shot Image Classification
Few-shot learning that trains image classifiers over few labeled examples per category is a challenging task.
Otem&Utem: Over- and Under-Translation Evaluation Metric for NMT
Although neural machine translation(NMT) yields promising translation performance, it unfortunately suffers from over- and under-translation is- sues [Tu et al., 2016], of which studies have become research hotspots in NMT.
Simplifying Neural Machine Translation with Addition-Subtraction Twin-Gated Recurrent Networks
Experiments on WMT14 translation tasks demonstrate that ATR-based neural machine translation can yield competitive performance on English- German and English-French language pairs in terms of both translation quality and speed.
The Disruptions of 5G on Data-driven Technologies and Applications
With 5G on the verge of being adopted as the next mobile network, there is a need to analyze its impact on the landscape of computing and data management.
Networking and Internet Architecture Databases Distributed, Parallel, and Cluster Computing
Blockchains vs. Distributed Databases: Dichotomy and Fusion
As blockchain evolves into another data management system, the natural question is how it compares against distributed database systems.
Databases Performance
Multi-View Matching Network for 6D Pose Estimation
Applications that interact with the real world such as augmented reality or robot manipulation require a good understanding of the location and pose of the surrounding objects.
Print Defect Mapping with Semantic Segmentation
Efficient automated print defect mapping is valuable to the printing industry since such defects directly influence customer-perceived printer quality and manually mapping them is cost-ineffective.
LoCEC: Local Community-based Edge Classification in Large Online Social Networks
To tackle the challenges, we propose a Local Community-based Edge Classification (LoCEC) framework that classifies user relationships in a social network into real-world social connection types.
Learning to Identify Follow-Up Questions in Conversational Question Answering
Despite recent progress in conversational question answering, most prior work does not focus on follow-up questions.
The Blessing and the Curse of the Noise behind Facial Landmark Annotations
The evolving algorithms for 2D facial landmark detection empower people to recognize faces, analyze facial expressions, etc.
MLCask: Efficient Management of Component Evolution in Collaborative Data Analytics Pipelines
In this paper, we identify two main challenges that arise during the deployment of machine learning pipelines, and address them with the design of versioning for an end-to-end analytics system MLCask.
Boosting High-Level Vision with Joint Compression Artifacts Reduction and Super-Resolution
In this paper, we aim to generate an artifact-free high-resolution image from a low-resolution one compressed with an arbitrary quality factor by exploring joint compression artifacts reduction (CAR) and super-resolution (SR) tasks.
A Co-Attentive Cross-Lingual Neural Model for Dialogue Breakdown Detection
One of the major challenges is that a dialogue system may generate an undesired utterance leading to a dialogue breakdown, which degrades the overall interaction quality.
A Semi-supervised Learning Approach with Two Teachers to Improve Breakdown Identification in Dialogues
We leverage unlabeled data to improve classification in student training where we employ two teachers to refine the labeling of unlabeled data through teacher-student learning in a bootstrapping manner.
CNNArginineMe: A CNN structure for training models for predicting arginine methylation sites based on the One-Hot encoding of peptide sequence
The deep-learning model CNN combined with the One-Hot coding showed the best performance, dubbed CNNArginineMe.
Generalization Ability of Wide Neural Networks on $\mathbb{R}$
We perform a study on the generalization ability of the wide two-layer ReLU neural network on $\mathbb{R}$.
Kernel interpolation generalizes poorly
One of the most interesting problems in the recent renaissance of the studies in kernel regression might be whether the kernel interpolation can generalize well, since it may help us understand the `benign overfitting henomenon' reported in the literature on deep networks.
On the Eigenvalue Decay Rates of a Class of Neural-Network Related Kernel Functions Defined on General Domains
In this paper, we provide a strategy to determine the eigenvalue decay rate (EDR) of a large class of kernel functions defined on a general domain rather than $\mathbb S^{d}$.
On the Optimality of Misspecified Kernel Ridge Regression
In the misspecified kernel ridge regression problem, researchers usually assume the underground true function $f_{\rho}^{*} \in [\mathcal{H}]^{s}$, a less-smooth interpolation space of a reproducing kernel Hilbert space (RKHS) $\mathcal{H}$ for some $s\in (0, 1)$.
Generalization Ability of Wide Residual Networks
This uniform convergence further guarantees that the generalization error of the residual network converges to that of the kernel regression with respect to the RNTK.
Safe Offline Reinforcement Learning with Real-Time Budget Constraints
Aiming at promoting the safe real-world deployment of Reinforcement Learning (RL), research on safe RL has made significant progress in recent years.
Optimal Rate of Kernel Regression in Large Dimensions
We perform a study on kernel regression for large-dimensional data (where the sample size $n$ is polynomially depending on the dimension $d$ of the samples, i. e., $n\asymp d^{\gamma}$ for some $\gamma >0$ ).
Optimal Rates of Kernel Ridge Regression under Source Condition in Large Dimensions
Motivated by the studies of neural networks (e. g., the neural tangent kernel theory), we perform a study on the large-dimensional behavior of kernel ridge regression (KRR) where the sample size $n \asymp d^{\gamma}$ for some $\gamma > 0$.
Generalization Error Curves for Analytic Spectral Algorithms under Power-law Decay
The generalization error curve of certain kernel regression method aims at determining the exact order of generalization error with various source condition, noise level and choice of the regularization parameter rather than the minimax rate.
Policy-regularized Offline Multi-objective Reinforcement Learning
In this paper, we aim to utilize only offline trajectory data to train a policy for multi-objective RL.
Off-Policy Primal-Dual Safe Reinforcement Learning
Results on benchmark tasks show that our method not only achieves an asymptotic performance comparable to state-of-the-art on-policy methods while using much fewer samples, but also significantly reduces constraint violation during training.
The Optimality of Kernel Classifiers in Sobolev Space
Kernel methods are widely used in machine learning, especially for classification problems.
The phase diagram of kernel interpolation in large dimensions
The generalization ability of kernel interpolation in large dimensions (i. e., $n \asymp d^{\gamma}$ for some $\gamma>0$) might be one of the most interesting problems in the recent renaissance of kernel regression, since it may help us understand the 'benign overfitting phenomenon' reported in the neural networks literature.
Improved Word Sense Disambiguation with Enhanced Sense Representations
Current state-of-the-art supervised word sense disambiguation (WSD) systems (such as GlossBERT and bi-encoder model) yield surprisingly good results by purely leveraging pre-trained language models and short dictionary definitions (or glosses) of the different word senses.
Ranked #2 on
Word Sense Disambiguation
on Supervised:
