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Found 12 papers, 1 papers with code
Learning to Inference Adaptively for Multimodal Large Language Models
Multimodal Large Language Models (MLLMs) have shown impressive capabilities in reasoning, yet come with substantial computational cost, limiting their deployment in resource-constrained settings.
The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape
Deep learning has shown incredible potential across a wide array of tasks, and accompanied by this growth has been an insatiable appetite for data.
SmartAdapt: Multi-Branch Object Detection Framework for Videos on Mobiles
In this paper, we ask, and answer, the wide-ranging question across all MBODFs: How to expose the right set of execution branches and then how to schedule the optimal one at inference time?
Virtuoso: Video-based Intelligence for real-time tuning on SOCs
First, the system does not consider energy consumption of the models while making a decision on which model to run.
Lerna: Transformer Architectures for Configuring Error Correction Tools for Short- and Long-Read Genome Sequencing
We also show that the alignment rate and assembly quality computed for the corrected reads are strongly negatively correlated with the perplexity, enabling the automated selection of k-mer values for better error correction, and hence, improved assembly quality.
TESSERACT: Gradient Flip Score to Secure Federated Learning Against Model Poisoning Attacks
The attack uses the intuition that simply by changing the sign of the gradient updates that the optimizer is computing, for a set of malicious clients, a model can be diverted from the optima to increase the test error rate.
Federated Action Recognition on Heterogeneous Embedded Devices
Federated learning allows a large number of devices to jointly learn a model without sharing data.
JANUS: Benchmarking Commercial and Open-Source Cloud and Edge Platforms for Object and Anomaly Detection Workloads
In addition, we also look at the pros and cons of some of the proprietary deep-learning object detection packages, such as Amazon Rekognition, Google Vision, and Azure Cognitive Services, to contrast with open-source and tunable solutions, such as Faster R-CNN (FRCNN).
ApproxDet: Content and Contention-Aware Approximate Object Detection for Mobiles
In this paper we introduce ApproxDet, an adaptive video object detection framework for mobile devices to meet accuracy-latency requirements in the face of changing content and resource contention scenarios.
Artificial Intelligence for Digital Agriculture at Scale: Techniques, Policies, and Challenges
Digital agriculture has the promise to transform agricultural throughput.
ApproxNet: Content and Contention-Aware Video Analytics System for Embedded Clients
None of the current approximation techniques for object classification DNNs can adapt to changing runtime conditions, e. g., changes in resource availability on the device, the content characteristics, or requirements from the user.
ATHENA: Automated Tuning of Genomic Error Correction Algorithms using Language Models
The performance of most error-correction algorithms that operate on genomic sequencer reads is dependent on the proper choice of its configuration parameters, such as the value of k in k-mer based techniques.
