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375

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Sustainable techniques to improve Data Quality for training image-based explanatory models for Recommender Systems

A new research study explores sustainable techniques to improve the data quality for training image-based explanatory models for Recommender Systems.
Current approaches in this domain often suffer from limitations due to sparse and noisy training data.
To address this, the study introduces three novel strategies for training data quality enhancement, including reliable negative training example selection, transform-based data augmentation, and text-to-image generative-based data augmentation.
Integration of these strategies in explainability models resulted in a 5% performance increase without compromising long-term sustainability.

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Arxiv

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Backdoor Graph Condensation

Graph condensation is a technique to improve the training efficiency for graph neural networks (GNNs).
Researchers have introduced an effective backdoor attack called BGC against graph condensation.
BGC targets representative nodes for poisoning and achieves a high attack success rate.
The attack proves resilient against multiple defense methods.

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Arxiv

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Krait: A Backdoor Attack Against Graph Prompt Tuning

Graph prompt tuning has emerged as a promising paradigm for transferring general graph knowledge from pre-trained models to downstream tasks.
A backdoor attack known as Krait has been introduced, which disguises benign graph prompts to evade detection.
Krait efficiently embeds triggers to a small fraction of training nodes, achieving high attack success rates without sacrificing clean accuracy.
The study analyzes how Krait can evade both classical and state-of-the-art defenses and provides insights for detecting and mitigating such attacks.

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Arxiv

308

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The impact of internal variability on benchmarking deep learning climate emulators

Full-complexity Earth system models (ESMs) are computationally expensive, limiting their use in exploring climate outcomes.
Efficient emulators that approximate ESMs are being used to map emissions onto climate outcomes.
A comparison between deep learning emulators and a linear regression-based emulator was conducted on ClimateBench, a popular benchmark for data-driven climate emulation.
The linear regression-based emulator outperformed the deep learning foundation model on 3 out of 4 regionally-resolved climate variables.

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Arxiv

130

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Monge-Kantorovich Fitting With Sobolev Budgets

The paper discusses the problem of approximating an n-dimensional probability measure with an m-dimensional measure.
The approach involves the use of Monge-Kantorovich (Wasserstein) p-cost to quantify the performance of the approximation.
The complexity is constrained by bounding the Sobolev norm of the coverable support by an 'f' function.
The study also presents a gradient analysis of the functional and proposes interpretations for regularization in improvement of training.

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Arxiv

260

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On the Implicit Relation Between Low-Rank Adaptation and Differential Privacy

Low-rank adaptation methods in natural language processing, such as LoRA and FLoRA, involve keeping pre-trained model weights fixed and incorporating trainable low-rank decomposition matrices into some layers of the transformer architecture, called adapters.
Researchers have found that the low-rank adaptation used in LoRA and FLoRA introduces random noise into the batch gradients with respect to the adapter parameters, leading to a variance in the injected noise that increases as the adaptation rank decreases.
The study establishes a relationship between low-rank adaptation and differential privacy, showing that the dynamics of low-rank adaptation is similar to differentially private fine-tuning of the adapters.
The researchers suggest that low-rank adaptation offers privacy protection without the high space complexity of differentially private stochastic gradient descent (DPSGD), providing an efficient alternative for privacy-preserving fine-tuning in NLP models.

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Arxiv

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Theoretical Insights into Fine-Tuning Attention Mechanism: Generalization and Optimization

Large Language Models (LLMs), built on Transformer architectures, exhibit remarkable generalization across a wide range of tasks.
Fine-tuning LLMs for specific tasks remains resource-intensive due to extensive parameterization.
Two remarkable phenomena related to the attention mechanism during fine-tuning of LLMs are investigated.
Insights from the investigation lead to a new strategy to improve fine-tuning efficiency in terms of storage and time.

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Arxiv

241

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RelChaNet: Neural Network Feature Selection using Relative Change Scores

RelChaNet is a supervised feature selection algorithm that utilizes neuron pruning and regrowth in a dense neural network's input layer.
For pruning, a relative change metric is used to measure the impact a feature has on the network.
In addition, an extension is proposed to dynamically adapt the size of the input layer during runtime.
Experimental results on 13 datasets demonstrate that RelChaNet outperforms existing methods, with a 2% increase in accuracy on the MNIST dataset.

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Arxiv

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Improving Neural Optimal Transport via Displacement Interpolation

Optimal Transport (OT) theory investigates the cost-minimizing transport map that moves a source distribution to a target distribution.
Existing methods for learning the optimal transport map using neural networks often experience training instability and sensitivity to hyperparameters.
A novel method called Displacement Interpolation Optimal Transport Model (DIOTM) is proposed to improve stability and achieve a better approximation of the OT Map.
DIOTM outperforms existing OT-based models on image-to-image translation tasks.

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Arxiv

185

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DDIL: Diversity Enhancing Diffusion Distillation With Imitation Learning

Diffusion models excel at generative modeling but have limitations in sampling due to multiple denoising network passes.
Co-variate shift is identified as a reason for poor performance of multi-step distilled models.
To address co-variate shift, the researchers propose a diffusion distillation within an imitation learning framework (DDIL).
DDIL enhances training distribution for distilling diffusion models, improving performance and stability.

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Arxiv

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MazeNet: An Accurate, Fast, and Scalable Deep Learning Solution for Steiner Minimum Trees

MazeNet is a deep learning-based method for solving the Obstacle Avoiding Rectilinear Steiner Minimum Tree (OARSMT) problem.
MazeNet reframes OARSMT as a maze-solving task and utilizes a recurrent convolutional neural network (RCNN).
MazeNet achieves perfect OARSMT-solving accuracy, reduces runtime compared to classical exact algorithms, and can handle more terminals than approximate algorithms.
The scalability of MazeNet allows for training on small mazes and solving larger mazes by replicating pre-trained blocks.

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Arxiv

237

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Trojan Cleansing with Neural Collapse

Trojan Cleansing with Neural Collapse
Trojan attacks are sophisticated training-time attacks on neural networks that embed backdoor triggers.
The researchers connect trojan attacks to Neural Collapse, a phenomenon that affects over-parameterized neural networks.
They designed a mechanism to cleanse trojan attacks from different network architectures and demonstrated its efficacy.

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Arxiv

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Machine Learning Analysis of Anomalous Diffusion

Machine learning techniques are increasingly being used for analyzing anomalous diffusion.
The review focuses on single trajectory characterization and representation learning.
Various machine learning methods, including classical and deep learning, are compared.
The study offers valuable perspectives for future research in the field.

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Arxiv

355

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COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation

Retrieval augmentation, the practice of retrieving additional data from large auxiliary pools, has emerged as an effective technique for enhancing model performance in the low-data regime.
Prior approaches have employed only nearest-neighbor based strategies for data selection, which retrieve auxiliary samples with high similarity to instances in the target task.
COBRA (COmBinatorial Retrieval Augmentation) is a new approach that employs an alternative CMI measure that considers both diversity and similarity to a target dataset for retrieval augmentation.
COBRA consistently outperforms previous retrieval approaches, providing significant gains in downstream model performance without incurring significant computational overhead.

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Arxiv

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ML Mule: Mobile-Driven Context-Aware Collaborative Learning

Artificial intelligence has been integrated into nearly every aspect of daily life, but often detached from individual users.
ML Mule proposes a mobile-driven approach to address privacy concerns and provide real-time, personalized experiences.
ML Mule utilizes individual mobile devices as 'mules' to train and transport model snapshots through physical spaces.
ML Mule converges faster and achieves higher model accuracy compared to existing methods.

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