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Found 32 papers, 12 papers with code
GOLD: Generalized Knowledge Distillation via Out-of-Distribution-Guided Language Data Generation
Knowledge distillation from LLMs is essential for the efficient deployment of language models.
Self-Healing Effects in OAM Beams Observed on a 28 GHz Experimental Link
The OAM beams have a helical phase and polarization structure and have conical amplitude shape in the far field.
ArchBERT: Bi-Modal Understanding of Neural Architectures and Natural Languages
Despite the success of these multi-modal language models with different modalities, there is no existing solution for neural network architectures and natural languages.
BioAct-Het: A Heterogeneous Siamese Neural Network for Bioactivity Prediction Using Novel Bioactivity Representatio
In particular, we introduce a novel representation for the bioactivity classes, called Bio-Prof, and enhance the original bioactivity data sets to tackle data scarcity.
Ranked #1 on
Molecular Property Prediction
on Tox21
ETran: Energy-Based Transferability Estimation
This is the first work that proposes transferability estimation for object detection task.
Ranked #1 on
Transferability
on classification benchmark
EnrichEvent: Enriching Social Data with Contextual Information for Emerging Event Extraction
Social platforms have emerged as crucial platforms for disseminating information and discussing real-life social events, offering researchers an excellent opportunity to design and implement novel event detection frameworks.
A Persian Benchmark for Joint Intent Detection and Slot Filling
To evaluate the effectiveness of our benchmark, we employ state-of-the-art methods for intent detection and slot filling.
A Deep Learning-Based Approach for Cell Outage Compensation in NOMA Networks
Cell outage compensation enables a network to react to a catastrophic cell failure quickly and serve users in the outage zone uninterruptedly.
E-LANG: Energy-Based Joint Inferencing of Super and Swift Language Models
As such, it can be applied to black-box pre-trained models without a need for architectural manipulations, reassembling of modules, or re-training.
Deep Learning meets Liveness Detection: Recent Advancements and Challenges
Hence, a new research area dealing with the development of more generalized as well as accurate models is increasingly attracting the attention of the research community and industry.
EBJR: Energy-Based Joint Reasoning for Adaptive Inference
To this end, we propose an Energy-Based Joint Reasoning (EBJR) framework that adaptively distributes the samples between shallow and deep models to achieve an accuracy close to the deep model, but latency close to the shallow one.
Advances and Challenges in Deep Lip Reading
Driven by deep learning techniques and large-scale datasets, recent years have witnessed a paradigm shift in automatic lip reading.
Bagging Supervised Autoencoder Classifier for Credit Scoring
The obtained results from our experiments on the benchmark and real-life credit scoring datasets illustrate the robustness and effectiveness of the Bagging Supervised Autoencoder Classifier in the classification of loan applicants that can be regarded as a positive development in credit scoring models.
Learned Image Compression with Gaussian-Laplacian-Logistic Mixture Model and Concatenated Residual Modules
However, due to the vast diversity of images, it is not optimal to use one model for all images, even different regions within one image.
Age of Information Aware VNF Scheduling in Industrial IoT Using Deep Reinforcement Learning
In this paper, we first utilize single agent low-complex compound action actor-critic RL to cover both discrete and continuous actions and jointly minimize VNF cost and AoI in terms of network resources under end-to end Quality of Service constraints.
A Compact Deep Learning Model for Face Spoofing Detection
Despite the results each of these techniques has achieved for a certain attack scenario or dataset, most of them still failed to generalized the problem for unseen conditions, as the efficiency of each is limited to certain type of presentation attacks and instruments (PAI).
Learned Multi-Resolution Variable-Rate Image Compression with Octave-based Residual Blocks
Recently deep learning-based image compression has shown the potential to outperform traditional codecs.
Twitter Spam Detection: A Systematic Review
Literature review analysis reveals that most of the existing methods rely on Machine Learning-based algorithms.
Dynamic Ensemble Learning for Credit Scoring: A Comparative Study
Automatic credit scoring, which assesses the probability of default by loan applicants, plays a vital role in peer-to-peer lending platforms to reduce the risk of lenders.
DRL-Based QoS-Aware Resource Allocation Scheme for Coexistence of Licensed and Unlicensed Users in LTE and Beyond
In this paper, we employ deep reinforcement learning to develop a novel radio resource allocation and packet scheduling scheme for different Quality of Service (QoS) requirements applicable to LTEadvanced and 5G networks.
Riccati updates for online linear quadratic control
We study an online setting of the linear quadratic Gaussian optimal control problem on a sequence of cost functions, where similar to classical online optimization, the future decisions are made by only knowing the cost in hindsight.
Generalized Octave Convolutions for Learned Multi-Frequency Image Compression
Learned image compression has recently shown the potential to outperform the standard codecs.
Deep Learning-based Image Compression with Trellis Coded Quantization
Recently many works attempt to develop image compression models based on deep learning architectures, where the uniform scalar quantizer (SQ) is commonly applied to the feature maps between the encoder and decoder.
Learned Variable-Rate Image Compression with Residual Divisive Normalization
Recently it has been shown that deep learning-based image compression has shown the potential to outperform traditional codecs.
MarlRank: Multi-agent Reinforced Learning to Rank
By defining reward as a function of NDCG, we can optimize our model directly on the ranking performance measure.
Using Contextual Information to Improve Blood Glucose Prediction
Person-generated data sources, such as actively contributed surveys as well as passively mined data from social media offer opportunity to capture such context, however the self-reported nature and sparsity of such data mean that such data are noisier and less specific than physiological measures such as blood glucose values themselves.
Improved Hybrid Layered Image Compression using Deep Learning and Traditional Codecs
Recently deep learning-based methods have been applied in image compression and achieved many promising results.
Detecting Target-Area Link-Flooding DDoS Attacks using Traffic Analysis and Supervised Learning
A novel class of extreme link-flooding DDoS (Distributed Denial of Service) attacks is designed to cut off entire geographical areas such as cities and even countries from the Internet by simultaneously targeting a selected set of network links.
From the User to the Medium: Neural Profiling Across Web Communities
Online communities provide a unique way for individuals to access information from those in similar circumstances, which can be critical for health conditions that require daily and personalized management.
Named Entity Disambiguation using Deep Learning on Graphs
We tackle \ac{NED} by comparing entities in short sentences with \wikidata{} graphs.
DSSLIC: Deep Semantic Segmentation-based Layered Image Compression
A compact representation of the input image is also generated and encoded as the first enhancement layer.
Semi-Recurrent CNN-based VAE-GAN for Sequential Data Generation
A semi-recurrent hybrid VAE-GAN model for generating sequential data is introduced.
