Paper tackles structure learning of sparse GGMs over multiple access networks.
problem Estimating sparse Gaussian Graphical Model structure from multiple local machines with limited communication.
method Proposes Signs and Uncoded methods for reliable structure learning under power and bandwidth limitations.
result Both methods can recover the structure with high probability for large enough sample size.
Deep learning improves SCMA decoding and codebook construction for 5G wireless networks.
problem Designing low complexity high accuracy decoding algorithms and constructing optimal SCMA codebooks.
method Proposed a deep neural network (DNN) called DL-SCMA and an autoencoder (AE-SCMA) to learn and reconstruct SCMA modulated signals.
result Deep learning SCMA decoder outperforms conventional algorithms in BER, SER, and computational complexity.
A novel Gradient-Based Multiple Access algorithm for distributed learning over fading channels.
problem Distributed learning over multiple access fading channels.
method Gradient-Based Multiple Access (GBMA) algorithm, using analog gradients and common shaping waveforms.
result GBMA can approach the convergence rate of centralized gradient descent in large networks.
Proposes a deep reinforcement learning framework for dynamic multichannel access.
problem Efficient use of limited spectral resources in dynamic multichannel access.
method Deep actor-critic reinforcement learning framework for both single-user and multi-user scenarios.
result Demonstrates improved performance and adaptive ability compared to existing methods.
Deep RL for dynamic pricing of express lanes considers multiple origins, destinations, and access locations.
problem Dynamic pricing of express lanes with multiple access points and traveler heterogeneity.
method Formulated as a POMDP, uses policy gradient methods and neural networks to determine stochastic tolls.
result Deep RL outperforms traditional methods in maximizing revenue and minimizing travel time.
Paper uses machine learning to optimize UAV deployment for traffic offloading.
problem Optimizing UAV deployment for efficient traffic offloading from ground BSs.
method LSTM for traffic prediction, KEG algorithm for service area determination, multi-access techniques comparison.
result RSMA reduces up to 24% total power consumption compared to conventional methods.
The problem of distributed learning and channel access is considered in a cognitive network with multiple secondary users. The availability statistics of the channels are initially unknown to the secondary users and are estimated using sensing decisions. There is no explicit information exchange or prior agreement amon…
Dynamic cell-free networks reduce complexity in serving many devices with distributed APs and DRL.
problem Designing efficient cell-free networks with many devices and APs.
method Dynamic architecture, SIC, DAS, DRL for optimization.
result DRL significantly improves performance in dynamic cell-free networks.
Paper addresses FL over MAC with DP constraints, proposing a novel consensus scheme.
problem Federated learning over a multiple access channel with differential privacy constraints.
method Proposes a novel consensus scheme using digital distributed stochastic gradient descent (D-DSGD) with artificial noise to preserve DP.
result Demonstrates improved convergence rate and DP level for a given MAC capacity.
BiQGEMM efficiently multiplies quantized DNN weights using lookup tables.
problem Efficiently multiplying quantized DNN weights on CPUs/GPUs with limited memory.
method BiQGEMM pre-computes and stores redundant intermediate results in lookup tables.
result BiQGEMM achieves lower overall computations and higher performance.
Optimal adversarial noise algorithms for multiple classifiers using game theory.
problem Designing robust attacks against multiple classifiers.
method Formulating the problem as a two-player, zero-sum game and using Multiplicative Weights Update framework with best response oracles.
result Demonstrated the effectiveness of randomization in adversarial attacks and optimal mixed strategies.
A measure of neural complexity quantifies how hard it is to access information across neurons.
problem Understanding how mutual information is distributed among neurons in neural networks.
method Partial Information Decomposition (PID) to disentangle contributions of single neurons, multiple neurons, and synergistic effects.
result Representational Complexity measures the difficulty of accessing information across multiple neurons.
With rapid development of the Internet, web contents become huge. Most of the websites are publicly available, and anyone can access the contents from anywhere such as workplace, home and even schools. Nevertheless, not all the web contents are appropriate for all users, especially children. An example of these content…
Model analyzes trading frictions in cap-and-trade markets, showing how they interact to affect market effectiveness.
problem Analyzing how trading frictions impact cap-and-trade market effectiveness.
method Developed a dynamic stochastic model with multiple trading frictions, characterized access choices in closed form, and quantified using EU ETS data.
result Trading frictions interact to amplify or dampen market responses, and their combined effect is non-additive.
A new method clusters subjects based on brain networks without vectorizing fMRI data.
problem Distortion of clustering results when simplifying fMRI data structure.
method Wishart mixture models for multiple-view clustering of brain networks.
result Identifies multiple underlying pairs of associations between subject clusters and brain sub-networks.
Wireless sensor networks are composed of distributed sensors that can be used for signal detection or classification. The likelihood functions of the hypotheses are often not known in advance, and decision rules have to be learned via supervised learning. A specific such algorithm is Fisher discriminant analysis (FDA),…
In our study, we demonstrate the synergy effect between convolutional neural networks and the multiplicity of SMILES. The model we propose, the so-called Convolutional Neural Fingerprint (CNF) model, reaches the accuracy of traditional descriptors such as Dragon (Mauri et al. [22]), RDKit (Landrum [18]), CDK2 (Willigha…
Dynamic sparseness reduces neural network computation by selectively omitting parts of computations.
problem Reducing the computational and memory footprint of neural networks.
method Combining dynamic sparseness with block-wise matrix-vector multiplications to selectively omit parts of computations.
result The proposed method outperforms static sparseness and achieves similar perplexities at half the computational cost.
We introduce a novel scheme to train binary convolutional neural networks (CNNs) -- CNNs with weights and activations constrained to {-1,+1} at run-time. It has been known that using binary weights and activations drastically reduce memory size and accesses, and can replace arithmetic operations with more efficient bit…
New framework uses MABs for better WLAN performance.
problem Maximizing enterprise WLAN performance with complex AP and station interactions.
method Empowered APs and stations with agents using Thompson sampling to learn optimal channel and AP selection.
result Adaptive framework using MABs outperforms static configurations in various scenarios.
Paper tackles caching in fog radio access networks using RL.
problem Distributed edge caching in fog radio access networks with fluctuating traffic demands.
method Q-learning framework for optimal caching policy, value function approximation.
result Proposed method outperforms traditional methods in simulations.
QFlow learns to prioritize video streaming to improve quality of experience.
problem Inconsistent video streaming quality due to network inefficiency.
method Develops a learning approach to dynamically allocate resources for video streaming.
result Demonstrates improved video quality for all clients at a wireless access point.
The paper develops a learning algorithm for distributed training and inference in wireless networks.
problem Challenges of leveraging machine learning in highly distributed wireless networks.
method Developed a learning algorithm and architecture for distributed training and inference.
result Inference propagates and fuses across a network, with benefits over state-of-the-art techniques.
Dynamic residual adapters improve performance across multiple latent domains without domain labels.
problem Overfitting to large domains and ignoring smaller ones in multi-domain learning.
method Dynamic residual adapters and augmentation strategies inspired by style transfer.
result Dynamic residual adapters significantly outperform standard models on multiple latent domains.
Optimal channel assignment in ad hoc networks with heterogeneous users.
problem Optimal allocation of channels in a decentralized network with unknown and varying channel characteristics.
method Developed algorithms for explore-and-commit and logarithmic regret, leveraging specific user transmissions and sensing.
result Achieved near-optimal allocation with high probability and logarithmic regret, significantly outperforming state-of-the-art methods.
Over-the-air computation (AirComp) shows great promise to support fast data fusion in Internet-of-Things (IoT) networks. AirComp typically computes desired functions of distributed sensing data by exploiting superposed data transmission in multiple access channels. To overcome its reliance on channel station informatio…
The paper proposes a new method to calibrate multiple computer models simultaneously.
problem Calibrating multiple computer models one at a time is inefficient.
method Developed a probabilistic framework using customized neural networks.
result Simultaneous calibration improves predictive accuracy but can be non-identifiable in high dimensions.
The FAIRnets Ontology makes neural networks findable, accessible, interoperable, and reusable.
problem The resource-intensive training of neural networks and the lack of training data availability.
method Development of FAIRnets Ontology to model neural networks on a meta-level and creation of a knowledge graph (FAIRnets) of over 18,400 neural networks.
result The FAIRnets Ontology and knowledge graph enable the reuse and recommendation of neural networks to data scientists.
Spatial separation of suspended particles based on contrast in their physical or chemical properties forms the basis of various biological assays performed on lab-on-achip devices. To electronically acquire this information, we have recently introduced a microfluidic sensing platform, called Microfluidic CODES, which c…
Improves key instance detection in MIL models by using neural network inversion with sparseness constraint.
problem Limited key instance detection performance in attention-based deep MIL models due to skewed attention scores.
method Sparse network inversion with a sparseness constraint incorporated into neural network inversion, solved by proximal gradient method.
result Significantly improved key instance detection performance while maintaining bag-level prediction performance.
A new method uses machine learning to optimize user pairing and association in multicell NOMA networks.
problem Optimizing user pairing and association in multicell non-orthogonal multiple access (NOMA) systems.
method Formulated as a combinatorial optimization problem, solved using a Pointer Network (PtrNet) trained with deep reinforcement learning.
result Achieves near-optimal performance in terms of aggregate data rate, outperforming random heuristics by up to 30%.
Regularizes RNNs to handle long-range dependencies and multiple time scales.
problem Identifying nonlinear dynamical systems with varying time scales and long-range dependencies.
method A simple regularization scheme for vanilla RNNs with ReLU activation.
result Regularized RNNs can solve long-range dependency problems and express slow time scales.
SmartDeal reduces energy and storage costs for deep neural networks.
problem Heavy parameterization of deep neural networks leads to inefficient use of DRAM.
method SmartDeal decomposes weights into a small basis matrix and a structurally sparse coefficient matrix, quantized to power-of-2.
result Up to 2.44x energy efficiency improvement in inference and 10.56x reduction in training energy.
In this paper, we present a new approach to learning cascaded classifiers for use in computing environments that involve networks of heterogeneous and resource-constrained, low-power embedded compute and sensing nodes. We present a generalization of the classical linear detection cascade to the case of tree-structured …
Dynamic spectrum access (DSA) is regarded as an effective and efficient technology to share radio spectrum among different networks. As a secondary user (SU), a DSA device will face two critical problems: avoiding causing harmful interference to primary users (PUs), and conducting effective interference coordination wi…
New methods improve graph classification accuracy using regional uncertainty.
problem Improving graph classification accuracy without labeled data.
method Proposed two new selection criteria for active learning: regional uncertainty and extended page-rank.
result Extended page-rank outperforms other methods when the fraction of labeled nodes is low.
Unified access package for fundamental physics datasets simplifies machine learning.
problem Lack of unified access to datasets from multiple fundamental physics disciplines.
method Unified Python package with common interface and reference models.
result Graph-based neural networks perform similarly to dedicated methods on various datasets.
Method analyzes deep neural network activations to explain adversarial examples.
problem Difficulty in interpreting deep neural network representations.
method Persistent homology over graphical activation structure.
result Adversarial examples are not semantic structure additions but dominant activation structure alterations.
MFIN networks improve crypto trading with multiple features.
problem Selecting and processing multiple features for effective trading.
method End-to-end framework using Multi-Factor Inception Networks (MFINs).
result MFINs learn uncorrelated, higher-Sharpe strategies not captured by traditional factors.
Study identifies latent variables and causal relationships from multiple environments.
problem Identify latent variables and causal relationships from multiple environments.
method Proposes algorithm LiNGCReL for identifying causal graph up to surrounded-node ambiguity.
result Identifies latent variables up to surrounded-node ambiguity (SNA) in linear causal models.
ViViT efficiently computes curvature for deep networks without approximations.
problem Efficiently computing curvature for deep networks without approximations.
method Leverages the GGN's low-rank structure without further approximations.
result ViViT allows for efficient computation of eigenvalues, eigenvectors, and directional derivatives.
We present a memory augmented neural network for natural language understanding: Neural Semantic Encoders. NSE is equipped with a novel memory update rule and has a variable sized encoding memory that evolves over time and maintains the understanding of input sequences through read}, compose and write operations. NSE c…
We propose an approach to address two issues that commonly occur during training of unsupervised GANs. First, since GANs use only a continuous latent distribution to embed multiple classes or clusters of data, they often do not correctly handle the structural discontinuity between disparate classes in a latent space. S…
Study shows algorithms benefit from limited target data with many source domains.
problem Adapting to new domains with scarce labeled target data.
method New family of model selection algorithms.
result Beneficial guarantees in scenarios with limited target data.
With access to large datasets, deep neural networks (DNN) have achieved human-level accuracy in image and speech recognition tasks. However, in chemistry, data is inherently small and fragmented. In this work, we develop an approach of using rule-based knowledge for training ChemNet, a transferable and generalizable de…
We present a method for conditional time series forecasting based on an adaptation of the recent deep convolutional WaveNet architecture. The proposed network contains stacks of dilated convolutions that allow it to access a broad range of history when forecasting, a ReLU activation function and conditioning is perform…
FELICIA uses a centralized adversary to improve synthetic medical image generation.
problem Collaborative learning with limited and biased data in medical image analysis.
method Federated generative modeling with a centralized adversary.
result Data owners can generate high-quality synthetic images with high utility without sharing real data.
A new algorithm speeds up neural network training with less data.
problem Slow convergence of traditional neural network training methods.
method Decouples hidden layers and updates connections through bootstrapping, resampling, and linear regression.
result Empirically shows faster convergence with fewer data points.