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AI&#39;s widespread integration has led to neural networks (NN) deployment on edge and similar limited-resource platforms for safety-critical scenarios. Yet, NN&#39;s fragility raises concerns about reliable inference. Moreover, constrained platforms demand compact networks. This study introduces VeriCompress, a tool that automates the search and training of compressed models with robustness guarantees. These models are well-suited for safety-critical applications and adhere to predefined architecture and size limitations, making them deployable on resource-restricted platforms. The method trains models 2-3 times faster than the state-of-the-art approaches, surpassing them by average accuracy and robustness gains of 15.1 and 9.8 percentage points, respectively. When deployed on a resource-restricted generic platform, these models require 5-8 times less memory and 2-4 times less inference time than models used in verified robustness literature. Our comprehensive evaluation across various model architectures and datasets, including MNIST, CIFAR, SVHN, and a relevant pedestrian detection dataset, showcases VeriCompress&#39;s capacity to identify compressed verified robust models with reduced computation overhead compared to current standards. This underscores its potential as a valuable tool for end users, such as developers of safety-critical applications on edge or Internet of Things platforms, empowering them to create suitable models for safety-critical, resource-constrained platforms in their respective domains.

AAAI 2024

VeriCompress: A Tool to Streamline the Synthesis of Verified Robust Compressed Neural Networks from Scratch

random initialization

formally verified robustness

model compression

AI's widespread integration has led to neural networks (NN) deployment on edge and similar limited-resource platforms for safety-critical scenarios. Yet, NN's fragility raises concerns about reliable inference. Moreover, constrained platforms demand compact networks. This study introduces VeriCompress, a tool that automates the search and training of compressed models with robustness guarantees. These models are well-suited for safety-critical applications and adhere to predefined architecture and size limitations, making them deployable on resource-restricted platforms. The method trains models 2-3 times faster than the state-of-the-art approaches, surpassing them by average accuracy and robustness gains of 15.1 and 9.8 percentage points, respectively. When deployed on a resource-restricted generic platform, these models require 5-8 times less memory and 2-4 times less inference time than models used in verified robustness literature. Our comprehensive evaluation across various model architectures and datasets, including MNIST, CIFAR, SVHN, and a relevant pedestrian detection dataset, showcases VeriCompress's capacity to identify compressed verified robust models with reduced computation overhead compared to current standards. This underscores its potential as a valuable tool for end users, such as developers of safety-critical applications on edge or Internet of Things platforms, empowering them to create suitable models for safety-critical, resource-constrained platforms in their respective domains.

technical paper

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.<br><br><br><br>

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

Communication overhead remains a significant challenge in federated learning due to frequent global model updates. Essentially, the update of the global model can be viewed as knowledge transfer. We aim to transfer more knowledge through a compact model while reducing communication overhead. In our study, we introduce a federated learning framework where clients pre-train large models locally and the server initializes a compact model to communicate. This compact model should be light in size but still have enough knowledge to refine the global model effectively. We facilitate the knowledge transfer from local to global models based on pre-training outcomes. Our experiments show that our approach significantly reduce communication overhead without sacrificing accuracy.

Knowledge Transfer via Compact Model in Federated Learning (Student Abstract)

Existing image-to-image translation methods perform less satisfactorily in the "day-night" domain due to insufficient scene feature study. To address this problem, we propose DNIT, which performs fine-grained handling of features by a nighttime image preprocessing (NIP) module and an edge fusion detection (EFD) module. The NIP module enhances brightness while minimizing noise, facilitating extracting content and style features. Meanwhile, the EFD module utilizes two types of edge images as additional constraints to optimize the generator. Experimental results show that we can generate more realistic and higher-quality images compared to other methods, proving the effectiveness of our DNIT.

DNIT: Enhancing Day-Night Image-to-Image Translation through Fine-Grained Feature Handling (Student Abstract)

Explainable Artificial Intelligence (XAI), a promising future technology in the field of healthcare, has attracted significant interest. Despite ongoing efforts in the development of XAI approaches, there has been inadequate evaluation of explanation effectiveness and no standardized framework for the evaluation has been established. This study aims to examine the relationship between subjective interpretability and perceived plausibility for various XAI explanations and to determine the factors affecting users' acceptance of the XAI explanation.

Evaluating the Effectiveness of Explainable Artificial Intelligence Approaches (Student Abstract)

Transcription factors (TFs) play a fundamental role in gene regulation by selectively binding to specific DNA sequences. Understanding the nature and behavior of these TFs is essential for insights into gene regulation dynamics. In this study, we introduce a robust multi-task learning framework specifically tailored to harness both TF-specific annotations and TF-related domain annotations, thereby enhancing the accuracy of TF predictions. Notably, we incorporate cutting-edge language models that have recently garnered attention for their outstanding performance across various fields, particularly in biological computations like protein sequence modeling. Comparative experimental analysis with existing models, DeepTFactor and TFpredict, reveals that our multi-task learning framework achieves an accuracy exceeding 92% across four evaluation metrics on the TF prediction task, surpassing both competitors. Our work marks a significant leap in the domain of TF prediction, enriching our comprehension of gene regulatory mechanisms and paving the way for the discovery of novel regulatory motifs.

Enhancing Transcription Factor Prediction through Multi-Task Learning (Student Abstract) | VIDEO

Conventional commonsense knowledge graph completion (CKGC) methods provide inadequate sequence when fine-tuning or generating stages and incorporate full fine-tuning, which fail to align with the autoregressive model's pre-training patterns and have insufficient parameter efficiency. Moreover, decoding through beam or greedy search produces low diversity and high similarity in generated tail entities. Hence, we resort to prefix-tuning and propose a lightweight, effective pipeline to enhance the quality and diversity of extracted commonsense knowledge. Precisely, we measure head entity similarity to yield and then concatenate top-k tuples before each target tuple for prefix-tuning the source LM, thereby improving the efficiency and speed for pretrained models; then, we design a penalty-tailored diverse beam search (p-DBS) for decoding tail entities, producing a greater quantity and diversity of generated commonsense tuples; besides, a filter strategy is utilized to filter out invalid commonsense knowledge. Through extensive automatic evaluations, including ChatGPT scoring, our method can extract diverse, novel, and accurate commonsense knowledge (CK).

Amplifying Diversity and Quality in Commonsense Knowledge Graph Completion (Student Abstract)

Pre-trained language models (PLMs) have greatly transformed various downstream tasks, yet frequently display social biases from training data, raising fairness concerns. Recent efforts to debias PLMs come with limitations: they either fine-tune the entire parameters in PLMs, which is time-consuming and disregards the expressiveness of PLMs, or ignore the reintroducing biases from downstream tasks when applying debiased models to them. Hence, we propose a two-stage pipeline to mitigate biases from both internal and downstream contexts while preserving expressiveness in language models. Specifically, for the debiasing procedure, we resort to continuous prefix-tuning, not fully fine-tuning the PLM, in which we design a debiasing term for optimization and an alignment term to keep words’ relative distances and ensure the model's expressiveness. For downstream tasks, we perform causal intervention across different demographic groups for invariant predictions. Results on three GLUE tasks show our method alleviates biases from internal and downstream contexts, while keeping PLM expressiveness intact.

Biases Mitigation and Expressiveness Preservation in Language Models: A Comprehensive Pipeline (Student Abstract)

Pre-trained language models have shown their high performance of text processing in intelligent customer service platforms. However, these models do not leverage domain specific information. In this paper, we propose icsPLMs optimized for intelligent customer service on both word and sentence levels. Our experimental results represent that using targeted strategies can further improve the performance of pre-trained language models in this field.

icsPLMs: Exploring Pre-trained Language Models in Intelligent Customer Service (Student Abstract) | VIDEO

In this evolving era of machine learning security, membership inference attacks have emerged as a potent threat to the confidentiality of sensitive data. In this attack, adversaries aim to determine whether a particular point was used during the training of a target model. This paper proposes a new method to gauge a data point’s membership in a model’s training set. Instead of correlating loss with membership, as is traditionally done, we have leveraged the fact that training examples generally exhibit higher confidence values when classified into their actual class. During training, the model is essentially being ’fit’ to the training data and might face particular difficulties in generalization to unseen data. This asymmetry leads to the model achieving higher confidence on the training data as it exploits the specific patterns and noise present in the training data. Our proposed approach leverages the confidence values generated by the machine-learning model. These confidence values provide a probabilistic measure of the model’s certainty in its predictions and can further be used to infer the membership of a given data point. Additionally, we also introduce another variant of our method that allows us to carry out this attack without knowing the ground truth(true class) of a given data point, thus offering an edge over existing label-dependent attack methods.

Confidence Is All You Need for MI Attacks (Student Abstract)

Fluid resuscitation is an initial treatment frequently employed to treat shock, restore lost blood, protect tissues from injury, and prevent organ dysfunction in critically ill patients. However, it is not without risk (e.g., overly aggressive resuscitation may cause organ damage and even death). We leverage machine learning models trained to assess sufficiency of resuscitation in laboratory animals subjected to induced hemorrhage and transfer them to use with human trauma patients. Our key takeaway is that animal experiments and models can inform human healthcare, especially when human data is limited or when collecting relevant human data via potentially harmful protocols is unfeasible.

Adapting Animal Models to Assess Sufficiency of Fluid Resuscitation in Humans (Student Abstract)

Recently, the field of Speech Enhancement has witnessed the success of diffusion-based generative models. However, these diffusion-based methods used to take multiple iterations to generate high-quality samples, leading to high computational costs and inefficiency. In this paper, we propose SDFEN (Shallow Diffusion for Fast spEech eNhancement), a novel approach for addressing the inefficiency problem while enhancing the quality of generated samples by reducing the iterative steps in the reverse process of diffusion method. Specifically, we introduce the shallow diffusion strategy initiating the reverse process with an adaptive time step to accelerate inference. In addition, a dedicated noisy predictor is further proposed to guide the adaptive selection of time step. Experiment results demonstrate the superiority of the proposed SDFEN in effectiveness and efficiency.

VeriCompress: A Tool to Streamline the Synthesis of Verified Robust Compressed Neural Networks from Scratch

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Knowledge Transfer via Compact Model in Federated Learning (Student Abstract)

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VeriCompress: A Tool to Streamline the Synthesis of Verified Robust Compressed Neural Networks from Scratch

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Next from AAAI 2024

Knowledge Transfer via Compact Model in Federated Learning (Student Abstract)

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads