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Found 33 papers, 15 papers with code
Loss Modeling for Multi-Annotator Datasets
Accounting for the opinions of all annotators of a dataset is critical for fairness.
Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs
We first characterize the proportion of data to sample from each region of a program's input space (corresponding to different execution paths of the program) based on the complexity of learning a surrogate of the corresponding execution path.
MASA-TCN: Multi-anchor Space-aware Temporal Convolutional Neural Networks for Continuous and Discrete EEG Emotion Recognition
A natural method is to learn the temporal dynamic patterns.
Aggregating Intrinsic Information to Enhance BCI Performance through Federated Learning
Though numerous research groups and institutes collect a multitude of EEG datasets for the same BCI task, sharing EEG data from multiple sites is still challenging due to the heterogeneity of devices.
An Efficient Sparse Inference Software Accelerator for Transformer-based Language Models on CPUs
We apply our sparse accelerator on widely-used Transformer-based language models including Bert-Mini, DistilBERT, Bert-Base, and BERT-Large.
Generalizability of PRS313 for breast cancer risk amongst non-Europeans in a Los Angeles biobank
We found that the PRS313 achieved overlapping Areas under the ROC Curve (AUCs) in females of Lantix (AUC, 0. 68; 95 CI, 0. 65-0. 71) and European ancestry (AUC, 0. 70; 95 CI, 0. 69-0. 71) but lower AUCs for the AFR and EAA populations (AFR: AUC, 0. 61; 95 CI, 0. 56-0. 65; EAA: AUC, 0. 64; 95 CI, 0. 60-0. 680).
Acela: Predictable Datacenter-level Maintenance Job Scheduling
While it is tempting to use prior machine learning techniques for predicting job duration, we find that the structure of the maintenance job scheduling problem creates a unique challenge.
Fast DistilBERT on CPUs
In this work, we propose a new pipeline for creating and running Fast Transformer models on CPUs, utilizing hardware-aware pruning, knowledge distillation, quantization, and our own Transformer inference runtime engine with optimized kernels for sparse and quantized operators.
SCOPE: Safe Exploration for Dynamic Computer Systems Optimization
Such solutions monitor past system executions to learn the system's behavior under different hardware resource allocations before dynamically tuning resources to optimize the application execution.
Cello: Efficient Computer Systems Optimization with Predictive Early Termination and Censored Regression
Our evaluation shows that compared to the state-of-the-art SEML approach in computer systems optimization, Cello improves latency by 1. 19X for minimizing latency under a power constraint, and improves energy by 1. 18X for minimizing energy under a latency constraint.
Continuous Emotion Recognition using Visual-audio-linguistic information: A Technical Report for ABAW3
The visual encoding from the visual block is concatenated with the attention feature to emphasize the visual information.
NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction
To predict stragglers accurately and early without labeled positive examples or assumptions on latency distributions, this paper presents NURD, a novel Negative-Unlabeled learning approach with Reweighting and Distribution-compensation that only trains on negative and unlabeled streaming data.
Statistical Learning for Individualized Asset Allocation
We show that our proposed Discretization and Regression with generalized fOlded concaVe penalty on Effect discontinuity (DROVE) approach enjoys desirable theoretical properties and allows for statistical inference of the optimal value associated with optimal decision-making.
Programming with Neural Surrogates of Programs
With surrogate adaptation, programmers develop a surrogate of a program then retrain that surrogate on a different task.
Sparse Fusion for Multimodal Transformers
Multimodal classification is a core task in human-centric machine learning.
Continuous Emotion Recognition with Audio-visual Leader-follower Attentive Fusion
We propose an audio-visual spatial-temporal deep neural network with: (1) a visual block containing a pretrained 2D-CNN followed by a temporal convolutional network (TCN); (2) an aural block containing several parallel TCNs; and (3) a leader-follower attentive fusion block combining the audio-visual information.
LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface
It captures temporal dynamics of EEG which then serves as input to the proposed local and global graph-filtering layers.
TSception: Capturing Temporal Dynamics and Spatial Asymmetry from EEG for Emotion Recognition
TSception consists of dynamic temporal, asymmetric spatial, and high-level fusion layers, which learn discriminative representations in the time and channel dimensions simultaneously.
Augmentation Strategies for Learning with Noisy Labels
In this paper, we evaluate different augmentation strategies for algorithms tackling the "learning with noisy labels" problem.
Ranked #8 on
Image Classification
on Clothing1M
(using extra training data)
Multiple Instance Segmentation in Brachial Plexus Ultrasound Image Using BPMSegNet
In this paper, a network called Brachial Plexus Multi-instance Segmentation Network (BPMSegNet) is proposed to identify different tissues (nerves, arteries, veins, muscles) in ultrasound images.
DeepKeyGen: A Deep Learning-based Stream Cipher Generator for Medical Image Encryption and Decryption
In this paper, a novel deep learning-based key generation network (DeepKeyGen) is proposed as a stream cipher generator to generate the private key, which can then be used for encrypting and decrypting of medical images.
A Multi-View Dynamic Fusion Framework: How to Improve the Multimodal Brain Tumor Segmentation from Multi-Views?
Moreover, the multi-view fusion loss, which consists of the segmentation loss, the transition loss and the decision loss, is proposed to facilitate the training process of multi-view learning networks so as to keep the consistency of appearance and space, not only in the process of fusing segmentation results, but also in the process of training the learning network.
Channel Pruning Guided by Spatial and Channel Attention for DNNs in Intelligent Edge Computing
Guided by the scale values generated by SCA for measuring channel importance, we further propose a new channel pruning approach called Channel Pruning guided by Spatial and Channel Attention (CPSCA).
Planimation
Planimation is a modular and extensible open source framework to visualise sequential solutions of planning problems specified in PDDL.
A polynomial-time algorithm for learning nonparametric causal graphs
We establish finite-sample guarantees for a polynomial-time algorithm for learning a nonlinear, nonparametric directed acyclic graphical (DAG) model from data.
DeepEDN: A Deep Learning-based Image Encryption and Decryption Network for Internet of Medical Things
Specifically, in DeepEDN, the Cycle-Generative Adversarial Network (Cycle-GAN) is employed as the main learning network to transfer the medical image from its original domain into the target domain.
TSception: A Deep Learning Framework for Emotion Detection Using EEG
TSception consists of temporal and spatial convolutional layers, which learn discriminative representations in the time and channel domains simultaneously.
Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning
On a large retrospective cohort, this mixture-based approach outperforms physician, kernel only, and DRL-only experts.
Dynamical systems theory for causal inference with application to synthetic control methods
In this setting, we propose to screen out control units that have a weak dynamical relationship to the single treated unit before the model is fit.
Methodology
Evaluating Reinforcement Learning Algorithms in Observational Health Settings
Much attention has been devoted recently to the development of machine learning algorithms with the goal of improving treatment policies in healthcare.
RUN:Residual U-Net for Computer-Aided Detection of Pulmonary Nodules without Candidate Selection
The early detection and early diagnosis of lung cancer are crucial to improve the survival rate of lung cancer patients.
Multiresolution Kernel Approximation for Gaussian Process Regression
Gaussian process regression generally does not scale to beyond a few thousands data points without applying some sort of kernel approximation method.
Adaptive Subgradient Methods for Online AUC Maximization
Despite their encouraging results reported, the existing online AUC maximization algorithms often adopt simple online gradient descent approaches that fail to exploit the geometrical knowledge of the data observed during the online learning process, and thus could suffer from relatively larger regret.
