categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2404.02474
| null | null |
http://arxiv.org/pdf/2404.02474v1
|
2024-04-03T05:31:59Z
|
2024-04-03T05:31:59Z
|
uTeBC-NLP at SemEval-2024 Task 9: Can LLMs be Lateral Thinkers?
|
Inspired by human cognition, Jiang et al.(2023c) create a benchmark for assessing LLMs' lateral thinking-thinking outside the box. Building upon this benchmark, we investigate how different prompting methods enhance LLMs' performance on this task to reveal their inherent power for outside-the-box thinking ability. Through participating in SemEval-2024, task 9, Sentence Puzzle sub-task, we explore prompt engineering methods: chain of thoughts (CoT) and direct prompting, enhancing with informative descriptions, and employing contextualizing prompts using a retrieval augmented generation (RAG) pipeline. Our experiments involve three LLMs including GPT-3.5, GPT-4, and Zephyr-7B-beta. We generate a dataset of thinking paths between riddles and options using GPT-4, validated by humans for quality. Findings indicate that compressed informative prompts enhance performance. Dynamic in-context learning enhances model performance significantly. Furthermore, fine-tuning Zephyr on our dataset enhances performance across other commonsense datasets, underscoring the value of innovative thinking.
|
[
"['Pouya Sadeghi' 'Amirhossein Abaskohi' 'Yadollah Yaghoobzadeh']"
] |
null | null |
2404.02476
| null | null |
http://arxiv.org/pdf/2404.02476v2
|
2024-04-11T06:00:27Z
|
2024-04-03T05:32:10Z
|
Deep Reinforcement Learning for Traveling Purchaser Problems
|
The traveling purchaser problem (TPP) is an important combinatorial optimization problem with broad applications. Due to the coupling between routing and purchasing, existing works on TPPs commonly address route construction and purchase planning simultaneously, which, however, leads to exact methods with high computational cost and heuristics with sophisticated design but limited performance. In sharp contrast, we propose a novel approach based on deep reinforcement learning (DRL), which addresses route construction and purchase planning separately, while evaluating and optimizing the solution from a global perspective. The key components of our approach include a bipartite graph representation for TPPs to capture the market-product relations, and a policy network that extracts information from the bipartite graph and uses it to sequentially construct the route. One significant benefit of our framework is that we can efficiently construct the route using the policy network, and once the route is determined, the associated purchasing plan can be easily derived through linear programming, while, leveraging DRL, we can train the policy network to optimize the global solution objective. Furthermore, by introducing a meta-learning strategy, the policy network can be trained stably on large-sized TPP instances, and generalize well across instances of varying sizes and distributions, even to much larger instances that are never seen during training. Experiments on various synthetic TPP instances and the TPPLIB benchmark demonstrate that our DRL-based approach can significantly outperform well-established TPP heuristics, reducing the optimality gap by 40%-90%, and also showing an advantage in runtime, especially on large-sized instances.
|
[
"['Haofeng Yuan' 'Rongping Zhu' 'Wanlu Yang' 'Shiji Song' 'Keyou You'\n 'Yuli Zhang']"
] |
null | null |
2404.02478
| null | null |
http://arxiv.org/pdf/2404.02478v1
|
2024-04-03T05:36:21Z
|
2024-04-03T05:36:21Z
|
FedSelect: Personalized Federated Learning with Customized Selection of
Parameters for Fine-Tuning
|
Standard federated learning approaches suffer when client data distributions have sufficient heterogeneity. Recent methods addressed the client data heterogeneity issue via personalized federated learning (PFL) - a class of FL algorithms aiming to personalize learned global knowledge to better suit the clients' local data distributions. Existing PFL methods usually decouple global updates in deep neural networks by performing personalization on particular layers (i.e. classifier heads) and global aggregation for the rest of the network. However, preselecting network layers for personalization may result in suboptimal storage of global knowledge. In this work, we propose FedSelect, a novel PFL algorithm inspired by the iterative subnetwork discovery procedure used for the Lottery Ticket Hypothesis. FedSelect incrementally expands subnetworks to personalize client parameters, concurrently conducting global aggregations on the remaining parameters. This approach enables the personalization of both client parameters and subnetwork structure during the training process. Finally, we show that FedSelect outperforms recent state-of-the-art PFL algorithms under challenging client data heterogeneity settings and demonstrates robustness to various real-world distributional shifts. Our code is available at https://github.com/lapisrocks/fedselect.
|
[
"['Rishub Tamirisa' 'Chulin Xie' 'Wenxuan Bao' 'Andy Zhou' 'Ron Arel'\n 'Aviv Shamsian']"
] |
null | null |
2404.02484
| null | null |
http://arxiv.org/pdf/2404.02484v2
|
2024-04-15T11:48:37Z
|
2024-04-03T05:44:03Z
|
New methods for drug synergy prediction: a mini-review
|
In this mini-review, we explore the new prediction methods for drug combination synergy relying on high-throughput combinatorial screens. The fast progress of the field is witnessed in the more than thirty original machine learning methods published since 2021, a clear majority of them based on deep learning techniques. We aim to put these papers under a unifying lens by highlighting the core technologies, the data sources, the input data types and synergy scores used in the methods, as well as the prediction scenarios and evaluation protocols that the papers deal with. Our finding is that the best methods accurately solve the synergy prediction scenarios involving known drugs or cell lines while the scenarios involving new drugs or cell lines still fall short of an accurate prediction level.
|
[
"['Fatemeh Abbasi' 'Juho Rousu']"
] |
null | null |
2404.02491
| null | null |
http://arxiv.org/pdf/2404.02491v4
|
2024-05-22T05:23:45Z
|
2024-04-03T05:58:57Z
|
Measuring Social Norms of Large Language Models
|
We present a new challenge to examine whether large language models understand social norms. In contrast to existing datasets, our dataset requires a fundamental understanding of social norms to solve. Our dataset features the largest set of social norm skills, consisting of 402 skills and 12,383 questions covering a wide set of social norms ranging from opinions and arguments to culture and laws. We design our dataset according to the K-12 curriculum. This enables the direct comparison of the social understanding of large language models to humans, more specifically, elementary students. While prior work generates nearly random accuracy on our benchmark, recent large language models such as GPT3.5-Turbo and LLaMA2-Chat are able to improve the performance significantly, only slightly below human performance. We then propose a multi-agent framework based on large language models to improve the models' ability to understand social norms. This method further improves large language models to be on par with humans. Given the increasing adoption of large language models in real-world applications, our finding is particularly important and presents a unique direction for future improvements.
|
[
"['Ye Yuan' 'Kexin Tang' 'Jianhao Shen' 'Ming Zhang' 'Chenguang Wang']"
] |
null | null |
2404.02499
| null | null |
http://arxiv.org/pdf/2404.02499v2
|
2024-05-13T09:20:26Z
|
2024-04-03T06:25:42Z
|
Learning Generalized Policies for Fully Observable Non-Deterministic
Planning Domains
|
General policies represent reactive strategies for solving large families of planning problems like the infinite collection of solvable instances from a given domain. Methods for learning such policies from a collection of small training instances have been developed successfully for classical domains. In this work, we extend the formulations and the resulting combinatorial methods for learning general policies over fully observable, non-deterministic (FOND) domains. We also evaluate the resulting approach experimentally over a number of benchmark domains in FOND planning, present the general policies that result in some of these domains, and prove their correctness. The method for learning general policies for FOND planning can actually be seen as an alternative FOND planning method that searches for solutions, not in the given state space but in an abstract space defined by features that must be learned as well.
|
[
"['Till Hofmann' 'Hector Geffner']"
] |
null | null |
2404.02508
| null | null |
http://arxiv.org/pdf/2404.02508v1
|
2024-04-03T06:53:27Z
|
2024-04-03T06:53:27Z
|
VIAssist: Adapting Multi-modal Large Language Models for Users with
Visual Impairments
|
Individuals with visual impairments, encompassing both partial and total difficulties in visual perception, are referred to as visually impaired (VI) people. An estimated 2.2 billion individuals worldwide are affected by visual impairments. Recent advancements in multi-modal large language models (MLLMs) have showcased their extraordinary capabilities across various domains. It is desirable to help VI individuals with MLLMs' great capabilities of visual understanding and reasoning. However, it is challenging for VI people to use MLLMs due to the difficulties in capturing the desirable images to fulfill their daily requests. For example, the target object is not fully or partially placed in the image. This paper explores how to leverage MLLMs for VI individuals to provide visual-question answers. VIAssist can identify undesired images and provide detailed actions. Finally, VIAssist can provide reliable answers to users' queries based on the images. Our results show that VIAssist provides +0.21 and +0.31 higher BERTScore and ROUGE scores than the baseline, respectively.
|
[
"['Bufang Yang' 'Lixing He' 'Kaiwei Liu' 'Zhenyu Yan']"
] |
null | null |
2404.02510
| null | null |
http://arxiv.org/pdf/2404.02510v1
|
2024-04-03T06:53:56Z
|
2024-04-03T06:53:56Z
|
An Interpretable Client Decision Tree Aggregation process for Federated
Learning
|
Trustworthy Artificial Intelligence solutions are essential in today's data-driven applications, prioritizing principles such as robustness, safety, transparency, explainability, and privacy among others. This has led to the emergence of Federated Learning as a solution for privacy and distributed machine learning. While decision trees, as self-explanatory models, are ideal for collaborative model training across multiple devices in resource-constrained environments such as federated learning environments for injecting interpretability in these models. Decision tree structure makes the aggregation in a federated learning environment not trivial. They require techniques that can merge their decision paths without introducing bias or overfitting while keeping the aggregated decision trees robust and generalizable. In this paper, we propose an Interpretable Client Decision Tree Aggregation process for Federated Learning scenarios that keeps the interpretability and the precision of the base decision trees used for the aggregation. This model is based on aggregating multiple decision paths of the decision trees and can be used on different decision tree types, such as ID3 and CART. We carry out the experiments within four datasets, and the analysis shows that the tree built with the model improves the local models, and outperforms the state-of-the-art.
|
[
"['Alberto Argente-Garrido' 'Cristina Zuheros' 'M. Victoria Luzón'\n 'Francisco Herrera']"
] |
null | null |
2404.02511
| null | null |
http://arxiv.org/pdf/2404.02511v1
|
2024-04-03T06:55:59Z
|
2024-04-03T06:55:59Z
|
Stochastic Constrained Decentralized Optimization for Machine Learning
with Fewer Data Oracles: a Gradient Sliding Approach
|
In modern decentralized applications, ensuring communication efficiency and privacy for the users are the key challenges. In order to train machine-learning models, the algorithm has to communicate to the data center and sample data for its gradient computation, thus exposing the data and increasing the communication cost. This gives rise to the need for a decentralized optimization algorithm that is communication-efficient and minimizes the number of gradient computations. To this end, we propose the primal-dual sliding with conditional gradient sliding framework, which is communication-efficient and achieves an $varepsilon$-approximate solution with the optimal gradient complexity of $O(1/sqrt{varepsilon}+sigma^2/{varepsilon^2})$ and $O(log(1/varepsilon)+sigma^2/varepsilon)$ for the convex and strongly convex setting respectively and an LO (Linear Optimization) complexity of $O(1/varepsilon^2)$ for both settings given a stochastic gradient oracle with variance $sigma^2$. Compared with the prior work cite{wai-fw-2017}, our framework relaxes the assumption of the optimal solution being a strict interior point of the feasible set and enjoys wider applicability for large-scale training using a stochastic gradient oracle. We also demonstrate the efficiency of our algorithms with various numerical experiments.
|
[
"['Hoang Huy Nguyen' 'Yan Li' 'Tuo Zhao']"
] |
null | null |
2404.02538
| null | null |
http://arxiv.org/pdf/2404.02538v2
|
2024-04-28T10:10:33Z
|
2024-04-03T07:50:53Z
|
Convergence Analysis of Flow Matching in Latent Space with Transformers
|
We present theoretical convergence guarantees for ODE-based generative models, specifically flow matching. We use a pre-trained autoencoder network to map high-dimensional original inputs to a low-dimensional latent space, where a transformer network is trained to predict the velocity field of the transformation from a standard normal distribution to the target latent distribution. Our error analysis demonstrates the effectiveness of this approach, showing that the distribution of samples generated via estimated ODE flow converges to the target distribution in the Wasserstein-2 distance under mild and practical assumptions. Furthermore, we show that arbitrary smooth functions can be effectively approximated by transformer networks with Lipschitz continuity, which may be of independent interest.
|
[
"['Yuling Jiao' 'Yanming Lai' 'Yang Wang' 'Bokai Yan']"
] |
null | null |
2404.02545
| null | null |
http://arxiv.org/pdf/2404.02545v1
|
2024-04-03T08:03:27Z
|
2024-04-03T08:03:27Z
|
Grid-Mapping Pseudo-Count Constraint for Offline Reinforcement Learning
|
Offline reinforcement learning learns from a static dataset without interacting with the environment, which ensures security and thus owns a good prospect of application. However, directly applying naive reinforcement learning methods usually fails in an offline environment due to function approximation errors caused by out-of-distribution(OOD) actions. To solve this problem, existing algorithms mainly penalize the Q-value of OOD actions, the quality of whose constraints also matter. Imprecise constraints may lead to suboptimal solutions, while precise constraints require significant computational costs. In this paper, we propose a novel count-based method for continuous domains, called Grid-Mapping Pseudo-Count method(GPC), to penalize the Q-value appropriately and reduce the computational cost. The proposed method maps the state and action space to discrete space and constrains their Q-values through the pseudo-count. It is theoretically proved that only a few conditions are needed to obtain accurate uncertainty constraints in the proposed method. Moreover, we develop a Grid-Mapping Pseudo-Count Soft Actor-Critic(GPC-SAC) algorithm using GPC under the Soft Actor-Critic(SAC) framework to demonstrate the effectiveness of GPC. The experimental results on D4RL benchmark datasets show that GPC-SAC has better performance and less computational cost compared to other algorithms.
|
[
"['Yi Shen' 'Hanyan Huang' 'Shan Xie']"
] |
null | null |
2404.02555
| null | null |
http://arxiv.org/pdf/2404.02555v1
|
2024-04-03T08:22:41Z
|
2024-04-03T08:22:41Z
|
An Interpretable Power System Transient Stability Assessment Method with
Expert Guiding Neural-Regression-Tree
|
Deep learning based transient stability assessment (TSA) has achieved great success, yet the lack of interpretability hinders its industrial application. Although a great number of studies have tried to explore the interpretability of network solutions, many problems still remain unsolved: (1) the difference between the widely accepted power system knowledge and the generated interpretive rules is large, (2) the probability characteristics of the neural network have not been fully considered during generating the interpretive rules, (3) the cost of the trade-off between accuracy and interpretability is too heavy to take. To address these issues, an interpretable power system Transient Stability Assessment method with Expert guiding Neural-Regression-Tree (TSA-ENRT) is proposed. TSA-ENRT utilizes an expert guiding nonlinear regression tree to approximate the neural network prediction and the neural network can be explained by the interpretive rules generated by the tree model. The nonlinearity of the expert guiding nonlinear regression tree is endowed with the extracted knowledge from a simple two-machine three-bus power system, which forms an expert knowledge base and thus the generated interpretive rules are more consistent with human cognition. Besides, the expert guiding tree model can build a bridge between the interpretive rules and the probability prediction of neural network in a regression way. By regularizing the neural network with the average decision length of ENRT, the association of the neural network and tree model is constructed in the model training level which provides a better trade-off between accuracy and interpretability. Extensive experiments indicate the interpretive rules generated by the proposed TSA-ENRT are highly consistent with the neural network prediction and more agreed with human expert cognition.
|
[
"['Hanxuan Wang' 'Na Lu' 'Zixuan Wang' 'Jiacheng Liu' 'Jun Liu']"
] |
null | null |
2404.02572
| null | null |
http://arxiv.org/pdf/2404.02572v2
|
2024-04-12T11:43:07Z
|
2024-04-03T08:47:32Z
|
Incremental Learning with Concept Drift Detection and Prototype-based
Embeddings for Graph Stream Classification
|
Data stream mining aims at extracting meaningful knowledge from continually evolving data streams, addressing the challenges posed by nonstationary environments, particularly, concept drift which refers to a change in the underlying data distribution over time. Graph structures offer a powerful modelling tool to represent complex systems, such as, critical infrastructure systems and social networks. Learning from graph streams becomes a necessity to understand the dynamics of graph structures and to facilitate informed decision-making. This work introduces a novel method for graph stream classification which operates under the general setting where a data generating process produces graphs with varying nodes and edges over time. The method uses incremental learning for continual model adaptation, selecting representative graphs (prototypes) for each class, and creating graph embeddings. Additionally, it incorporates a loss-based concept drift detection mechanism to recalculate graph prototypes when drift is detected.
|
[
"['Kleanthis Malialis' 'Jin Li' 'Christos G. Panayiotou'\n 'Marios M. Polycarpou']"
] |
null | null |
2404.02577
| null | null |
http://arxiv.org/pdf/2404.02577v1
|
2024-04-03T08:53:42Z
|
2024-04-03T08:53:42Z
|
Solving a Real-World Optimization Problem Using Proximal Policy
Optimization with Curriculum Learning and Reward Engineering
|
We present a proximal policy optimization (PPO) agent trained through curriculum learning (CL) principles and meticulous reward engineering to optimize a real-world high-throughput waste sorting facility. Our work addresses the challenge of effectively balancing the competing objectives of operational safety, volume optimization, and minimizing resource usage. A vanilla agent trained from scratch on these multiple criteria fails to solve the problem due to its inherent complexities. This problem is particularly difficult due to the environment's extremely delayed rewards with long time horizons and class (or action) imbalance, with important actions being infrequent in the optimal policy. This forces the agent to anticipate long-term action consequences and prioritize rare but rewarding behaviours, creating a non-trivial reinforcement learning task. Our five-stage CL approach tackles these challenges by gradually increasing the complexity of the environmental dynamics during policy transfer while simultaneously refining the reward mechanism. This iterative and adaptable process enables the agent to learn a desired optimal policy. Results demonstrate that our approach significantly improves inference-time safety, achieving near-zero safety violations in addition to enhancing waste sorting plant efficiency.
|
[
"['Abhijeet Pendyala' 'Asma Atamna' 'Tobias Glasmachers']"
] |
null | null |
2404.02583
| null | null |
http://arxiv.org/pdf/2404.02583v1
|
2024-04-03T09:08:15Z
|
2024-04-03T09:08:15Z
|
Transformer-based Stagewise Decomposition for Large-Scale Multistage
Stochastic Optimization
|
Solving large-scale multistage stochastic programming (MSP) problems poses a significant challenge as commonly used stagewise decomposition algorithms, including stochastic dual dynamic programming (SDDP), face growing time complexity as the subproblem size and problem count increase. Traditional approaches approximate the value functions as piecewise linear convex functions by incrementally accumulating subgradient cutting planes from the primal and dual solutions of stagewise subproblems. Recognizing these limitations, we introduce TranSDDP, a novel Transformer-based stagewise decomposition algorithm. This innovative approach leverages the structural advantages of the Transformer model, implementing a sequential method for integrating subgradient cutting planes to approximate the value function. Through our numerical experiments, we affirm TranSDDP's effectiveness in addressing MSP problems. It efficiently generates a piecewise linear approximation for the value function, significantly reducing computation time while preserving solution quality, thus marking a promising progression in the treatment of large-scale multistage stochastic programming problems.
|
[
"['Chanyeong Kim' 'Jongwoong Park' 'Hyunglip Bae' 'Woo Chang Kim']"
] |
null | null |
2404.02591
| null | null |
http://arxiv.org/pdf/2404.02591v1
|
2024-04-03T09:15:38Z
|
2024-04-03T09:15:38Z
|
Adaptive Sampling Policies Imply Biased Beliefs: A Generalization of the
Hot Stove Effect
|
The Hot Stove Effect is a negativity bias resulting from the adaptive character of learning. The mechanism is that learning algorithms that pursue alternatives with positive estimated values, but avoid alternatives with negative estimated values, will correct errors of overestimation but fail to correct errors of underestimation. Here, we generalize the theory behind the Hot Stove Effect to settings in which negative estimates do not necessarily lead to avoidance but to a smaller sample size (i.e., a learner selects fewer of alternative B if B is believed to be inferior but does not entirely avoid B). We formally demonstrate that the negativity bias remains in this set-up. We also show there is a negativity bias for Bayesian learners in the sense that most such learners underestimate the expected value of an alternative.
|
[
"['Jerker Denrell']"
] |
null | null |
2404.02595
| null | null |
http://arxiv.org/pdf/2404.02595v2
|
2024-05-01T10:04:21Z
|
2024-04-03T09:19:46Z
|
QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection
|
This study introduces the Quantum Federated Neural Network for Financial Fraud Detection (QFNN-FFD), a cutting-edge framework merging Quantum Machine Learning (QML) and quantum computing with Federated Learning (FL) for financial fraud detection. Using quantum technologies' computational power and the robust data privacy protections offered by FL, QFNN-FFD emerges as a secure and efficient method for identifying fraudulent transactions within the financial sector. Implementing a dual-phase training model across distributed clients enhances data integrity and enables superior performance metrics, achieving precision rates consistently above 95%. Additionally, QFNN-FFD demonstrates exceptional resilience by maintaining an impressive 80% accuracy, highlighting its robustness and readiness for real-world applications. This combination of high performance, security, and robustness against noise positions QFNN-FFD as a transformative advancement in financial technology solutions and establishes it as a new benchmark for privacy-focused fraud detection systems. This framework facilitates the broader adoption of secure, quantum-enhanced financial services and inspires future innovations that could use QML to tackle complex challenges in other areas requiring high confidentiality and accuracy.
|
[
"['Nouhaila Innan' 'Alberto Marchisio' 'Muhammad Shafique' 'Mohamed Bennai']"
] |
null | null |
2404.02621
| null | null |
http://arxiv.org/pdf/2404.02621v1
|
2024-04-03T10:19:53Z
|
2024-04-03T10:19:53Z
|
Polynomial Graphical Lasso: Learning Edges from Gaussian
Graph-Stationary Signals
|
This paper introduces Polynomial Graphical Lasso (PGL), a new approach to learning graph structures from nodal signals. Our key contribution lies in modeling the signals as Gaussian and stationary on the graph, enabling the development of a graph-learning formulation that combines the strengths of graphical lasso with a more encompassing model. Specifically, we assume that the precision matrix can take any polynomial form of the sought graph, allowing for increased flexibility in modeling nodal relationships. Given the resulting complexity and nonconvexity of the resulting optimization problem, we (i) propose a low-complexity algorithm that alternates between estimating the graph and precision matrices, and (ii) characterize its convergence. We evaluate the performance of PGL through comprehensive numerical simulations using both synthetic and real data, demonstrating its superiority over several alternatives. Overall, this approach presents a significant advancement in graph learning and holds promise for various applications in graph-aware signal analysis and beyond.
|
[
"['Andrei Buciulea' 'Jiaxi Ying' 'Antonio G. Marques' 'Daniel P. Palomar']"
] |
null | null |
2404.02625
| null | null |
http://arxiv.org/pdf/2404.02625v1
|
2024-04-03T10:29:06Z
|
2024-04-03T10:29:06Z
|
A Differentiable Integer Linear Programming Solver for Explanation-Based
Natural Language Inference
|
Integer Linear Programming (ILP) has been proposed as a formalism for encoding precise structural and semantic constraints for Natural Language Inference (NLI). However, traditional ILP frameworks are non-differentiable, posing critical challenges for the integration of continuous language representations based on deep learning. In this paper, we introduce a novel approach, named Diff-Comb Explainer, a neuro-symbolic architecture for explanation-based NLI based on Differentiable BlackBox Combinatorial Solvers (DBCS). Differently from existing neuro-symbolic solvers, Diff-Comb Explainer does not necessitate a continuous relaxation of the semantic constraints, enabling a direct, more precise, and efficient incorporation of neural representations into the ILP formulation. Our experiments demonstrate that Diff-Comb Explainer achieves superior performance when compared to conventional ILP solvers, neuro-symbolic black-box solvers, and Transformer-based encoders. Moreover, a deeper analysis reveals that Diff-Comb Explainer can significantly improve the precision, consistency, and faithfulness of the constructed explanations, opening new opportunities for research on neuro-symbolic architectures for explainable and transparent NLI in complex domains.
|
[
"['Mokanarangan Thayaparan' 'Marco Valentino' 'André Freitas']"
] |
null | null |
2404.02629
| null | null |
http://arxiv.org/pdf/2404.02629v1
|
2024-04-03T10:36:08Z
|
2024-04-03T10:36:08Z
|
Effector: A Python package for regional explanations
|
Global feature effect methods explain a model outputting one plot per feature. The plot shows the average effect of the feature on the output, like the effect of age on the annual income. However, average effects may be misleading when derived from local effects that are heterogeneous, i.e., they significantly deviate from the average. To decrease the heterogeneity, regional effects provide multiple plots per feature, each representing the average effect within a specific subspace. For interpretability, subspaces are defined as hyperrectangles defined by a chain of logical rules, like age's effect on annual income separately for males and females and different levels of professional experience. We introduce Effector, a Python library dedicated to regional feature effects. Effector implements well-established global effect methods, assesses the heterogeneity of each method and, based on that, provides regional effects. Effector automatically detects subspaces where regional effects have reduced heterogeneity. All global and regional effect methods share a common API, facilitating comparisons between them. Moreover, the library's interface is extensible so new methods can be easily added and benchmarked. The library has been thoroughly tested, ships with many tutorials (https://xai-effector.github.io/) and is available under an open-source license at PyPi (https://pypi.org/project/effector/) and Github (https://github.com/givasile/effector).
|
[
"['Vasilis Gkolemis' 'Christos Diou' 'Eirini Ntoutsi' 'Theodore Dalamagas'\n 'Bernd Bischl' 'Julia Herbinger' 'Giuseppe Casalicchio']"
] |
null | null |
2404.02649
| null | null |
http://arxiv.org/pdf/2404.02649v2
|
2024-07-14T02:20:59Z
|
2024-04-03T11:21:23Z
|
On the Importance of Uncertainty in Decision-Making with Large Language
Models
|
We investigate the role of uncertainty in decision-making problems with natural language as input. For such tasks, using Large Language Models as agents has become the norm. However, none of the recent approaches employ any additional phase for estimating the uncertainty the agent has about the world during the decision-making task. We focus on a fundamental decision-making framework with natural language as input, which is the one of contextual bandits, where the context information consists of text. As a representative of the approaches with no uncertainty estimation, we consider an LLM bandit with a greedy policy, which picks the action corresponding to the largest predicted reward. We compare this baseline to LLM bandits that make active use of uncertainty estimation by integrating the uncertainty in a Thompson Sampling policy. We employ different techniques for uncertainty estimation, such as Laplace Approximation, Dropout, and Epinets. We empirically show on real-world data that the greedy policy performs worse than the Thompson Sampling policies. These findings suggest that, while overlooked in the LLM literature, uncertainty plays a fundamental role in bandit tasks with LLMs.
|
[
"['Nicolò Felicioni' 'Lucas Maystre' 'Sina Ghiassian' 'Kamil Ciosek']"
] |
null | null |
2404.02650
| null | null |
http://arxiv.org/pdf/2404.02650v1
|
2024-04-03T11:25:20Z
|
2024-04-03T11:25:20Z
|
Towards detecting unanticipated bias in Large Language Models
|
Over the last year, Large Language Models (LLMs) like ChatGPT have become widely available and have exhibited fairness issues similar to those in previous machine learning systems. Current research is primarily focused on analyzing and quantifying these biases in training data and their impact on the decisions of these models, alongside developing mitigation strategies. This research largely targets well-known biases related to gender, race, ethnicity, and language. However, it is clear that LLMs are also affected by other, less obvious implicit biases. The complex and often opaque nature of these models makes detecting such biases challenging, yet this is crucial due to their potential negative impact in various applications. In this paper, we explore new avenues for detecting these unanticipated biases in LLMs, focusing specifically on Uncertainty Quantification and Explainable AI methods. These approaches aim to assess the certainty of model decisions and to make the internal decision-making processes of LLMs more transparent, thereby identifying and understanding biases that are not immediately apparent. Through this research, we aim to contribute to the development of fairer and more transparent AI systems.
|
[
"['Anna Kruspe']"
] |
null | null |
2404.02660
| null | null |
http://arxiv.org/pdf/2404.02660v1
|
2024-04-03T11:49:43Z
|
2024-04-03T11:49:43Z
|
Adversarial Attacks and Dimensionality in Text Classifiers
|
Adversarial attacks on machine learning algorithms have been a key deterrent to the adoption of AI in many real-world use cases. They significantly undermine the ability of high-performance neural networks by forcing misclassifications. These attacks introduce minute and structured perturbations or alterations in the test samples, imperceptible to human annotators in general, but trained neural networks and other models are sensitive to it. Historically, adversarial attacks have been first identified and studied in the domain of image processing. In this paper, we study adversarial examples in the field of natural language processing, specifically text classification tasks. We investigate the reasons for adversarial vulnerability, particularly in relation to the inherent dimensionality of the model. Our key finding is that there is a very strong correlation between the embedding dimensionality of the adversarial samples and their effectiveness on models tuned with input samples with same embedding dimension. We utilize this sensitivity to design an adversarial defense mechanism. We use ensemble models of varying inherent dimensionality to thwart the attacks. This is tested on multiple datasets for its efficacy in providing robustness. We also study the problem of measuring adversarial perturbation using different distance metrics. For all of the aforementioned studies, we have run tests on multiple models with varying dimensionality and used a word-vector level adversarial attack to substantiate the findings.
|
[
"['Nandish Chattopadhyay' 'Atreya Goswami' 'Anupam Chattopadhyay']"
] |
null | null |
2404.02684
| null | null |
http://arxiv.org/pdf/2404.02684v1
|
2024-04-03T12:27:36Z
|
2024-04-03T12:27:36Z
|
Cross-Architecture Transfer Learning for Linear-Cost Inference
Transformers
|
Recently, multiple architectures has been proposed to improve the efficiency of the Transformer Language Models through changing the design of the self-attention block to have a linear-cost inference (LCI). A notable approach in this realm is the State-Space Machines (SSMs) architecture, which showed on-par performance on language modeling tasks with the self-attention transformers. However, such an architectural change requires a full pretraining of the weights from scratch, which incurs a huge cost to researchers and practitioners who want to use the new architectures. In the more traditional linear attention works, it has been proposed to approximate full attention with linear attention by swap-and-finetune framework. Motivated by this approach, we propose Cross-Architecture Transfer Learning (XATL), in which the weights of the shared components between LCI and self-attention-based transformers, such as layernorms, MLPs, input/output embeddings, are directly transferred to the new architecture from already pre-trained model parameters. We experimented the efficacy of the method on varying sizes and alternative attention architectures and show that methodabbr significantly reduces the training time up to 2.5x times and converges to a better minimum with up to 2.6% stronger model on the LM benchmarks within the same compute budget.
|
[
"['Sehyun Choi']"
] |
null | null |
2404.02688
| null | null |
http://arxiv.org/pdf/2404.02688v1
|
2024-04-03T12:36:25Z
|
2024-04-03T12:36:25Z
|
Reinforcement Learning in Categorical Cybernetics
|
We show that several major algorithms of reinforcement learning (RL) fit into the framework of categorical cybernetics, that is to say, parametrised bidirectional processes. We build on our previous work in which we show that value iteration can be represented by precomposition with a certain optic. The outline of the main construction in this paper is: (1) We extend the Bellman operators to parametrised optics that apply to action-value functions and depend on a sample. (2) We apply a representable contravariant functor, obtaining a parametrised function that applies the Bellman iteration. (3) This parametrised function becomes the backward pass of another parametrised optic that represents the model, which interacts with an environment via an agent. Thus, parametrised optics appear in two different ways in our construction, with one becoming part of the other. As we show, many of the major classes of algorithms in RL can be seen as different extremal cases of this general setup: dynamic programming, Monte Carlo methods, temporal difference learning, and deep RL. We see this as strong evidence that this approach is a natural one and believe that it will be a fruitful way to think about RL in the future.
|
[
"['Jules Hedges' 'Riu Rodríguez Sakamoto']"
] |
null | null |
2404.02690
| null | null |
http://arxiv.org/pdf/2404.02690v1
|
2024-04-03T12:37:34Z
|
2024-04-03T12:37:34Z
|
Attention is Naturally Sparse with Gaussian Distributed Input
|
The computational intensity of Large Language Models (LLMs) is a critical bottleneck, primarily due to the $O(n^2)$ complexity of the attention mechanism in transformer architectures. Addressing this, sparse attention emerges as a key innovation, aiming to reduce computational load while maintaining model performance. This study presents a rigorous theoretical analysis of the sparsity in attention scores within LLMs, particularly under the framework of Gaussian inputs. By establishing a set of foundational assumptions and employing a methodical theoretical approach, we unravel the intrinsic characteristics of attention score sparsity and its implications on computational efficiency. Our main contribution lies in providing a detailed theoretical examination of how sparsity manifests in attention mechanisms, offering insights into the potential trade-offs between computational savings and model effectiveness. This work not only advances our understanding of sparse attention but also provides a scaffold for future research in optimizing the computational frameworks of LLMs, paving the way for more scalable and efficient AI systems.
|
[
"['Yichuan Deng' 'Zhao Song' 'Chiwun Yang']"
] |
null | null |
2404.02692
| null | null |
http://arxiv.org/pdf/2404.02692v1
|
2024-04-03T12:39:37Z
|
2024-04-03T12:39:37Z
|
Automated Inference of Graph Transformation Rules
|
The explosion of data available in life sciences is fueling an increasing demand for expressive models and computational methods. Graph transformation is a model for dynamic systems with a large variety of applications. We introduce a novel method of the graph transformation model construction, combining generative and dynamical viewpoints to give a fully automated data-driven model inference method. The method takes the input dynamical properties, given as a "snapshot" of the dynamics encoded by explicit transitions, and constructs a compatible model. The obtained model is guaranteed to be minimal, thus framing the approach as model compression (from a set of transitions into a set of rules). The compression is permissive to a lossy case, where the constructed model is allowed to exhibit behavior outside of the input transitions, thus suggesting a completion of the input dynamics. The task of graph transformation model inference is naturally highly challenging due to the combinatorics involved. We tackle the exponential explosion by proposing a heuristically minimal translation of the task into a well-established problem, set cover, for which highly optimized solutions exist. We further showcase how our results relate to Kolmogorov complexity expressed in terms of graph transformation.
|
[
"['Jakob L. Andersen' 'Akbar Davoodi' 'Rolf Fagerberg' 'Christoph Flamm'\n 'Walter Fontana' 'Juri Kolčák' 'Christophe V. F. P. Laurent'\n 'Daniel Merkle' 'Nikolai Nøjgaard']"
] |
null | null |
2404.02696
| null | null |
http://arxiv.org/pdf/2404.02696v1
|
2024-04-03T12:50:45Z
|
2024-04-03T12:50:45Z
|
Deep Privacy Funnel Model: From a Discriminative to a Generative
Approach with an Application to Face Recognition
|
In this study, we apply the information-theoretic Privacy Funnel (PF) model to the domain of face recognition, developing a novel method for privacy-preserving representation learning within an end-to-end training framework. Our approach addresses the trade-off between obfuscation and utility in data protection, quantified through logarithmic loss, also known as self-information loss. This research provides a foundational exploration into the integration of information-theoretic privacy principles with representation learning, focusing specifically on the face recognition systems. We particularly highlight the adaptability of our framework with recent advancements in face recognition networks, such as AdaFace and ArcFace. In addition, we introduce the Generative Privacy Funnel ($mathsf{GenPF}$) model, a paradigm that extends beyond the traditional scope of the PF model, referred to as the Discriminative Privacy Funnel ($mathsf{DisPF}$). This $mathsf{GenPF}$ model brings new perspectives on data generation methods with estimation-theoretic and information-theoretic privacy guarantees. Complementing these developments, we also present the deep variational PF (DVPF) model. This model proposes a tractable variational bound for measuring information leakage, enhancing the understanding of privacy preservation challenges in deep representation learning. The DVPF model, associated with both $mathsf{DisPF}$ and $mathsf{GenPF}$ models, sheds light on connections with various generative models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Diffusion models. Complementing our theoretical contributions, we release a reproducible PyTorch package, facilitating further exploration and application of these privacy-preserving methodologies in face recognition systems.
|
[
"['Behrooz Razeghi' 'Parsa Rahimi' 'Sébastien Marcel']"
] |
null | null |
2404.02717
| null | null |
http://arxiv.org/pdf/2404.02717v1
|
2024-04-03T13:20:24Z
|
2024-04-03T13:20:24Z
|
Automatic Prompt Selection for Large Language Models
|
Large Language Models (LLMs) can perform various natural language processing tasks with suitable instruction prompts. However, designing effective prompts manually is challenging and time-consuming. Existing methods for automatic prompt optimization either lack flexibility or efficiency. In this paper, we propose an effective approach to automatically select the optimal prompt for a given input from a finite set of synthetic candidate prompts. Our approach consists of three steps: (1) clustering the training data and generating candidate prompts for each cluster using an LLM-based prompt generator; (2) synthesizing a dataset of input-prompt-output tuples for training a prompt evaluator to rank the prompts based on their relevance to the input; (3) using the prompt evaluator to select the best prompt for a new input at test time. Our approach balances prompt generality-specificity and eliminates the need for resource-intensive training and inference. It demonstrates competitive performance on zero-shot question-answering datasets: GSM8K, MultiArith, and AQuA.
|
[
"['Viet-Tung Do' 'Van-Khanh Hoang' 'Duy-Hung Nguyen' 'Shahab Sabahi'\n 'Jeff Yang' 'Hajime Hotta' 'Minh-Tien Nguyen' 'Hung Le']"
] |
null | null |
2404.02719
| null | null |
http://arxiv.org/pdf/2404.02719v1
|
2024-04-03T13:21:58Z
|
2024-04-03T13:21:58Z
|
Can We Understand Plasticity Through Neural Collapse?
|
This paper explores the connection between two recently identified phenomena in deep learning: plasticity loss and neural collapse. We analyze their correlation in different scenarios, revealing a significant association during the initial training phase on the first task. Additionally, we introduce a regularization approach to mitigate neural collapse, demonstrating its effectiveness in alleviating plasticity loss in this specific setting.
|
[
"['Guglielmo Bonifazi' 'Iason Chalas' 'Gian Hess' 'Jakub Łucki']"
] |
null | null |
2404.02722
| null | null |
http://arxiv.org/pdf/2404.02722v2
|
2024-06-10T09:13:29Z
|
2024-04-03T13:22:47Z
|
On-line conformalized neural networks ensembles for probabilistic
forecasting of day-ahead electricity prices
|
Probabilistic electricity price forecasting (PEPF) is subject of increasing interest, following the demand for proper quantification of prediction uncertainty, to support the operation in complex power markets with increasing share of renewable generation. Distributional neural networks ensembles have been recently shown to outperform state of the art PEPF benchmarks. Still, they require critical reliability enhancements, as fail to pass the coverage tests at various steps on the prediction horizon. In this work, we propose a novel approach to PEPF, extending the state of the art neural networks ensembles based methods through conformal inference based techniques, deployed within an on-line recalibration procedure. Experiments have been conducted on multiple market regions, achieving day-ahead forecasts with improved hourly coverage and stable probabilistic scores.
|
[
"['Alessandro Brusaferri' 'Andrea Ballarino' 'Luigi Grossi'\n 'Fabrizio Laurini']"
] |
null | null |
2404.02726
| null | null |
http://arxiv.org/pdf/2404.02726v1
|
2024-04-03T13:27:54Z
|
2024-04-03T13:27:54Z
|
Harnessing the Power of Large Vision Language Models for Synthetic Image
Detection
|
In recent years, the emergence of models capable of generating images from text has attracted considerable interest, offering the possibility of creating realistic images from text descriptions. Yet these advances have also raised concerns about the potential misuse of these images, including the creation of misleading content such as fake news and propaganda. This study investigates the effectiveness of using advanced vision-language models (VLMs) for synthetic image identification. Specifically, the focus is on tuning state-of-the-art image captioning models for synthetic image detection. By harnessing the robust understanding capabilities of large VLMs, the aim is to distinguish authentic images from synthetic images produced by diffusion-based models. This study contributes to the advancement of synthetic image detection by exploiting the capabilities of visual language models such as BLIP-2 and ViTGPT2. By tailoring image captioning models, we address the challenges associated with the potential misuse of synthetic images in real-world applications. Results described in this paper highlight the promising role of VLMs in the field of synthetic image detection, outperforming conventional image-based detection techniques. Code and models can be found at https://github.com/Mamadou-Keita/VLM-DETECT.
|
[
"['Mamadou Keita' 'Wassim Hamidouche' 'Hassen Bougueffa' 'Abdenour Hadid'\n 'Abdelmalik Taleb-Ahmed']"
] |
null | null |
2404.02728
| null | null |
http://arxiv.org/pdf/2404.02728v1
|
2024-04-03T13:28:52Z
|
2024-04-03T13:28:52Z
|
Unsupervised Learning of Effective Actions in Robotics
|
Learning actions that are relevant to decision-making and can be executed effectively is a key problem in autonomous robotics. Current state-of-the-art action representations in robotics lack proper effect-driven learning of the robot's actions. Although successful in solving manipulation tasks, deep learning methods also lack this ability, in addition to their high cost in terms of memory or training data. In this paper, we propose an unsupervised algorithm to discretize a continuous motion space and generate "action prototypes", each producing different effects in the environment. After an exploration phase, the algorithm automatically builds a representation of the effects and groups motions into action prototypes, where motions more likely to produce an effect are represented more than those that lead to negligible changes. We evaluate our method on a simulated stair-climbing reinforcement learning task, and the preliminary results show that our effect driven discretization outperforms uniformly and randomly sampled discretizations in convergence speed and maximum reward.
|
[
"['Marko Zaric' 'Jakob Hollenstein' 'Justus Piater' 'Erwan Renaudo']"
] |
null | null |
2404.02729
| null | null |
http://arxiv.org/pdf/2404.02729v1
|
2024-04-03T13:29:12Z
|
2024-04-03T13:29:12Z
|
Learning Sequence Attractors in Recurrent Networks with Hidden Neurons
|
The brain is targeted for processing temporal sequence information. It remains largely unclear how the brain learns to store and retrieve sequence memories. Here, we study how recurrent networks of binary neurons learn sequence attractors to store predefined pattern sequences and retrieve them robustly. We show that to store arbitrary pattern sequences, it is necessary for the network to include hidden neurons even though their role in displaying sequence memories is indirect. We develop a local learning algorithm to learn sequence attractors in the networks with hidden neurons. The algorithm is proven to converge and lead to sequence attractors. We demonstrate that the network model can store and retrieve sequences robustly on synthetic and real-world datasets. We hope that this study provides new insights in understanding sequence memory and temporal information processing in the brain.
|
[
"['Yao Lu' 'Si Wu']"
] |
null | null |
2404.02754
| null | null |
http://arxiv.org/pdf/2404.02754v1
|
2024-04-03T13:56:33Z
|
2024-04-03T13:56:33Z
|
Continual Learning of Numerous Tasks from Long-tail Distributions
|
Continual learning, an important aspect of artificial intelligence and machine learning research, focuses on developing models that learn and adapt to new tasks while retaining previously acquired knowledge. Existing continual learning algorithms usually involve a small number of tasks with uniform sizes and may not accurately represent real-world learning scenarios. In this paper, we investigate the performance of continual learning algorithms with a large number of tasks drawn from a task distribution that is long-tail in terms of task sizes. We design one synthetic dataset and two real-world continual learning datasets to evaluate the performance of existing algorithms in such a setting. Moreover, we study an overlooked factor in continual learning, the optimizer states, e.g. first and second moments in the Adam optimizer, and investigate how it can be used to improve continual learning performance. We propose a method that reuses the optimizer states in Adam by maintaining a weighted average of the second moments from previous tasks. We demonstrate that our method, compatible with most existing continual learning algorithms, effectively reduces forgetting with only a small amount of additional computational or memory costs, and provides further improvements on existing continual learning algorithms, particularly in a long-tail task sequence.
|
[
"['Liwei Kang' 'Wee Sun Lee']"
] |
null | null |
2404.02759
| null | null |
http://arxiv.org/pdf/2404.02759v1
|
2024-04-03T14:05:39Z
|
2024-04-03T14:05:39Z
|
Unsupervised Occupancy Learning from Sparse Point Cloud
|
Implicit Neural Representations have gained prominence as a powerful framework for capturing complex data modalities, encompassing a wide range from 3D shapes to images and audio. Within the realm of 3D shape representation, Neural Signed Distance Functions (SDF) have demonstrated remarkable potential in faithfully encoding intricate shape geometry. However, learning SDFs from 3D point clouds in the absence of ground truth supervision remains a very challenging task. In this paper, we propose a method to infer occupancy fields instead of SDFs as they are easier to learn from sparse inputs. We leverage a margin-based uncertainty measure to differentially sample from the decision boundary of the occupancy function and supervise the sampled boundary points using the input point cloud. We further stabilize the optimization process at the early stages of the training by biasing the occupancy function towards minimal entropy fields while maximizing its entropy at the input point cloud. Through extensive experiments and evaluations, we illustrate the efficacy of our proposed method, highlighting its capacity to improve implicit shape inference with respect to baselines and the state-of-the-art using synthetic and real data.
|
[
"['Amine Ouasfi' 'Adnane Boukhayma']"
] |
null | null |
2404.02761
| null | null |
http://arxiv.org/pdf/2404.02761v3
|
2024-04-17T10:56:48Z
|
2024-04-03T14:07:02Z
|
AQuA -- Combining Experts' and Non-Experts' Views To Assess Deliberation
Quality in Online Discussions Using LLMs
|
Measuring the quality of contributions in political online discussions is crucial in deliberation research and computer science. Research has identified various indicators to assess online discussion quality, and with deep learning advancements, automating these measures has become feasible. While some studies focus on analyzing specific quality indicators, a comprehensive quality score incorporating various deliberative aspects is often preferred. In this work, we introduce AQuA, an additive score that calculates a unified deliberative quality score from multiple indices for each discussion post. Unlike other singular scores, AQuA preserves information on the deliberative aspects present in comments, enhancing model transparency. We develop adapter models for 20 deliberative indices, and calculate correlation coefficients between experts' annotations and the perceived deliberativeness by non-experts to weigh the individual indices into a single deliberative score. We demonstrate that the AQuA score can be computed easily from pre-trained adapters and aligns well with annotations on other datasets that have not be seen during training. The analysis of experts' vs. non-experts' annotations confirms theoretical findings in the social science literature.
|
[
"['Maike Behrendt' 'Stefan Sylvius Wagner' 'Marc Ziegele' 'Lena Wilms'\n 'Anke Stoll' 'Dominique Heinbach' 'Stefan Harmeling']"
] |
null | null |
2404.02779
| null | null |
http://arxiv.org/pdf/2404.02779v1
|
2024-04-03T14:47:48Z
|
2024-04-03T14:47:48Z
|
Federated Computing -- Survey on Building Blocks, Extensions and Systems
|
In response to the increasing volume and sensitivity of data, traditional centralized computing models face challenges, such as data security breaches and regulatory hurdles. Federated Computing (FC) addresses these concerns by enabling collaborative processing without compromising individual data privacy. This is achieved through a decentralized network of devices, each retaining control over its data, while participating in collective computations. The motivation behind FC extends beyond technical considerations to encompass societal implications. As the need for responsible AI and ethical data practices intensifies, FC aligns with the principles of user empowerment and data sovereignty. FC comprises of Federated Learning (FL) and Federated Analytics (FA). FC systems became more complex over time and they currently lack a clear definition and taxonomy describing its moving pieces. Current surveys capture domain-specific FL use cases, describe individual components in an FC pipeline individually or decoupled from each other, or provide a quantitative overview of the number of published papers. This work surveys more than 150 papers to distill the underlying structure of FC systems with their basic building blocks, extensions, architecture, environment, and motivation. We capture FL and FA systems individually and point out unique difference between those two.
|
[
"['René Schwermer' 'Ruben Mayer' 'Hans-Arno Jacobsen']"
] |
null | null |
2404.02785
| null | null |
http://arxiv.org/pdf/2404.02785v1
|
2024-04-03T14:55:17Z
|
2024-04-03T14:55:17Z
|
Domain Generalization through Meta-Learning: A Survey
|
Deep neural networks (DNNs) have revolutionized artificial intelligence but often lack performance when faced with out-of-distribution (OOD) data, a common scenario due to the inevitable domain shifts in real-world applications. This limitation stems from the common assumption that training and testing data share the same distribution-an assumption frequently violated in practice. Despite their effectiveness with large amounts of data and computational power, DNNs struggle with distributional shifts and limited labeled data, leading to overfitting and poor generalization across various tasks and domains. Meta-learning presents a promising approach by employing algorithms that acquire transferable knowledge across various tasks for fast adaptation, eliminating the need to learn each task from scratch. This survey paper delves into the realm of meta-learning with a focus on its contribution to domain generalization. We first clarify the concept of meta-learning for domain generalization and introduce a novel taxonomy based on the feature extraction strategy and the classifier learning methodology, offering a granular view of methodologies. Through an exhaustive review of existing methods and underlying theories, we map out the fundamentals of the field. Our survey provides practical insights and an informed discussion on promising research directions, paving the way for future innovation in meta-learning for domain generalization.
|
[
"['Arsham Gholamzadeh Khoee' 'Yinan Yu' 'Robert Feldt']"
] |
null | null |
2404.02810
| null | null |
http://arxiv.org/pdf/2404.02810v2
|
2024-05-08T01:40:25Z
|
2024-04-03T15:31:18Z
|
Generative-Enhanced Heterogeneous Graph Contrastive Learning
|
Heterogeneous Graphs (HGs) can effectively model complex relationships in the real world by multi-type nodes and edges. In recent years, inspired by self-supervised learning, contrastive Heterogeneous Graphs Neural Networks (HGNNs) have shown great potential by utilizing data augmentation and contrastive discriminators for downstream tasks. However, data augmentation is still limited due to the graph data's integrity. Furthermore, the contrastive discriminators remain sampling bias and lack local heterogeneous information. To tackle the above limitations, we propose a novel Generative-Enhanced Heterogeneous Graph Contrastive Learning (GHGCL). Specifically, we first propose a heterogeneous graph generative learning enhanced contrastive paradigm. This paradigm includes: 1) A contrastive view augmentation strategy by using a masked autoencoder. 2) Position-aware and semantics-aware positive sample sampling strategy for generating hard negative samples. 3) A hierarchical contrastive learning strategy for capturing local and global information. Furthermore, the hierarchical contrastive learning and sampling strategies aim to constitute an enhanced contrastive discriminator under the generative-contrastive perspective. Finally, we compare our model with seventeen baselines on eight real-world datasets. Our model outperforms the latest contrastive and generative baselines on node classification and link prediction tasks. To reproduce our work, we have open-sourced our code at https://anonymous.4open.science/r/GC-HGNN-E50C.
|
[
"['Yu Wang' 'Lei Sang' 'Yi Zhang' 'Yiwen Zhang']"
] |
null | null |
2404.02822
| null | null |
http://arxiv.org/pdf/2404.02822v2
|
2024-04-04T11:23:59Z
|
2024-04-03T15:55:27Z
|
Identifying Climate Targets in National Laws and Policies using Machine
Learning
|
Quantified policy targets are a fundamental element of climate policy, typically characterised by domain-specific and technical language. Current methods for curating comprehensive views of global climate policy targets entail significant manual effort. At present there are few scalable methods for extracting climate targets from national laws or policies, which limits policymakers' and researchers' ability to (1) assess private and public sector alignment with global goals and (2) inform policy decisions. In this paper we present an approach for extracting mentions of climate targets from national laws and policies. We create an expert-annotated dataset identifying three categories of target ('Net Zero', 'Reduction' and 'Other' (e.g. renewable energy targets)) and train a classifier to reliably identify them in text. We investigate bias and equity impacts related to our model and identify specific years and country names as problematic features. Finally, we investigate the characteristics of the dataset produced by running this classifier on the Climate Policy Radar (CPR) dataset of global national climate laws and policies and UNFCCC submissions, highlighting the potential of automated and scalable data collection for existing climate policy databases and supporting further research. Our work represents a significant upgrade in the accessibility of these key climate policy elements for policymakers and researchers. We publish our model at https://huggingface.co/ClimatePolicyRadar/national-climate-targets and related dataset at https://huggingface.co/datasets/ClimatePolicyRadar/national-climate-targets.
|
[
"['Matyas Juhasz' 'Tina Marchand' 'Roshan Melwani' 'Kalyan Dutia'\n 'Sarah Goodenough' 'Harrison Pim' 'Henry Franks']"
] |
null | null |
2404.02823
| null | null |
http://arxiv.org/pdf/2404.02823v1
|
2024-04-03T15:55:39Z
|
2024-04-03T15:55:39Z
|
Conifer: Improving Complex Constrained Instruction-Following Ability of
Large Language Models
|
The ability of large language models (LLMs) to follow instructions is crucial to real-world applications. Despite recent advances, several studies have highlighted that LLMs struggle when faced with challenging instructions, especially those that include complex constraints, hindering their effectiveness in various tasks. To address this challenge, we introduce Conifer, a novel instruction tuning dataset, designed to enhance LLMs to follow multi-level instructions with complex constraints. Utilizing GPT-4, we curate the dataset by a series of LLM-driven refinement processes to ensure high quality. We also propose a progressive learning scheme that emphasizes an easy-to-hard progression, and learning from process feedback. Models trained with Conifer exhibit remarkable improvements in instruction-following abilities, especially for instructions with complex constraints. On several instruction-following benchmarks, our 7B model outperforms the state-of-the-art open-source 7B models, even exceeds the performance of models 10 times larger on certain metrics. All the code and Conifer dataset are available at https://www.github.com/ConiferLM/Conifer.
|
[
"['Haoran Sun' 'Lixin Liu' 'Junjie Li' 'Fengyu Wang' 'Baohua Dong'\n 'Ran Lin' 'Ruohui Huang']"
] |
null | null |
2404.02827
| null | null |
http://arxiv.org/pdf/2404.02827v2
|
2024-05-22T15:23:33Z
|
2024-04-03T15:59:42Z
|
BAdam: A Memory Efficient Full Parameter Optimization Method for Large
Language Models
|
This work presents BAdam, an optimization method that leverages the block coordinate descent framework with Adam as the inner solver. BAdam offers a memory efficient approach to the full parameter finetuning of large language models. We conduct theoretical convergence analysis for BAdam in the deterministic case. Experimentally, we apply BAdam to instruction-tune the Llama 2-7B and Llama 3-8B models using a single RTX3090-24GB GPU. The results confirm BAdam's efficiency in terms of memory and running time. Additionally, the convergence verification indicates that BAdam exhibits superior convergence behavior compared to LoRA. Furthermore, the downstream performance evaluation using the MT-bench shows that BAdam modestly surpasses LoRA and more substantially outperforms LOMO. Finally, we compare BAdam with Adam on a medium-sized task, i.e., finetuning RoBERTa-large on the SuperGLUE benchmark. The results demonstrate that BAdam is capable of narrowing the performance gap with Adam more effectively than LoRA. Our code is available at https://github.com/Ledzy/BAdam.
|
[
"['Qijun Luo' 'Hengxu Yu' 'Xiao Li']"
] |
null | null |
2404.02852
| null | null |
http://arxiv.org/pdf/2404.02852v1
|
2024-04-03T16:33:42Z
|
2024-04-03T16:33:42Z
|
Toward Inference-optimal Mixture-of-Expert Large Language Models
|
Mixture-of-Expert (MoE) based large language models (LLMs), such as the recent Mixtral and DeepSeek-MoE, have shown great promise in scaling model size without suffering from the quadratic growth of training cost of dense transformers. Like dense models, training MoEs requires answering the same question: given a training budget, what is the optimal allocation on the model size and number of tokens? We study the scaling law of MoE-based LLMs regarding the relations between the model performance, model size, dataset size, and the expert degree. Echoing previous research studying MoE in different contexts, we observe the diminishing return of increasing the number of experts, but this seems to suggest we should scale the number of experts until saturation, as the training cost would remain constant, which is problematic during inference time. We propose to amend the scaling law of MoE by introducing inference efficiency as another metric besides the validation loss. We find that MoEs with a few (4/8) experts are the most serving efficient solution under the same performance, but costs 2.5-3.5x more in training. On the other hand, training a (16/32) expert MoE much smaller (70-85%) than the loss-optimal solution, but with a larger training dataset is a promising setup under a training budget.
|
[
"['Longfei Yun' 'Yonghao Zhuang' 'Yao Fu' 'Eric P Xing' 'Hao Zhang']"
] |
null | null |
2404.02865
| null | null |
http://arxiv.org/pdf/2404.02865v1
|
2024-04-03T16:57:26Z
|
2024-04-03T16:57:26Z
|
End-To-End Self-tuning Self-supervised Time Series Anomaly Detection
|
Time series anomaly detection (TSAD) finds many applications such as monitoring environmental sensors, industry KPIs, patient biomarkers, etc. A two-fold challenge for TSAD is a versatile and unsupervised model that can detect various different types of time series anomalies (spikes, discontinuities, trend shifts, etc.) without any labeled data. Modern neural networks have outstanding ability in modeling complex time series. Self-supervised models in particular tackle unsupervised TSAD by transforming the input via various augmentations to create pseudo anomalies for training. However, their performance is sensitive to the choice of augmentation, which is hard to choose in practice, while there exists no effort in the literature on data augmentation tuning for TSAD without labels. Our work aims to fill this gap. We introduce TSAP for TSA "on autoPilot", which can (self-)tune augmentation hyperparameters end-to-end. It stands on two key components: a differentiable augmentation architecture and an unsupervised validation loss to effectively assess the alignment between augmentation type and anomaly type. Case studies show TSAP's ability to effectively select the (discrete) augmentation type and associated (continuous) hyperparameters. In turn, it outperforms established baselines, including SOTA self-supervised models, on diverse TSAD tasks exhibiting different anomaly types.
|
[
"['Boje Deforce' 'Meng-Chieh Lee' 'Bart Baesens' 'Estefanía Serral Asensio'\n 'Jaemin Yoo' 'Leman Akoglu']"
] |
null | null |
2404.02866
| null | null |
http://arxiv.org/pdf/2404.02866v3
|
2024-06-17T21:22:59Z
|
2024-04-03T16:58:03Z
|
Guarantees of confidentiality via Hammersley-Chapman-Robbins bounds
|
Protecting privacy during inference with deep neural networks is possible by adding noise to the activations in the last layers prior to the final classifiers or other task-specific layers. The activations in such layers are known as "features" (or, less commonly, as "embeddings" or "feature embeddings"). The added noise helps prevent reconstruction of the inputs from the noisy features. Lower bounding the variance of every possible unbiased estimator of the inputs quantifies the confidentiality arising from such added noise. Convenient, computationally tractable bounds are available from classic inequalities of Hammersley and of Chapman and Robbins -- the HCR bounds. Numerical experiments indicate that the HCR bounds are on the precipice of being effectual for small neural nets with the data sets, "MNIST" and "CIFAR-10," which contain 10 classes each for image classification. The HCR bounds appear to be insufficient on their own to guarantee confidentiality of the inputs to inference with standard deep neural nets, "ResNet-18" and "Swin-T," pre-trained on the data set, "ImageNet-1000," which contains 1000 classes. Supplementing the addition of noise to features with other methods for providing confidentiality may be warranted in the case of ImageNet. In all cases, the results reported here limit consideration to amounts of added noise that incur little degradation in the accuracy of classification from the noisy features. Thus, the added noise enhances confidentiality without much reduction in the accuracy on the task of image classification.
|
[
"['Kamalika Chaudhuri' 'Chuan Guo' 'Laurens van der Maaten'\n 'Saeed Mahloujifar' 'Mark Tygert']"
] |
null | null |
2404.02869
| null | null |
http://arxiv.org/pdf/2404.02869v1
|
2024-04-03T17:05:41Z
|
2024-04-03T17:05:41Z
|
Human Activity Recognition using Smartphones
|
Human Activity Recognition is a subject of great research today and has its applications in remote healthcare, activity tracking of the elderly or the disables, calories burnt tracking etc. In our project, we have created an Android application that recognizes the daily human activities and calculate the calories burnt in real time. We first captured labeled triaxial acceleration readings for different daily human activities from the smartphone's embedded accelerometer. These readings were preprocessed using a median filter. 42 features were extracted using various methods. We then tested various machine learning algorithms along with dimensionality reduction. Finally, in our Android application, we used the machine learning algorithm and a subset of features that provided maximum accuracy and minimum model building time. This is used for real-time activity recognition and calculation of calories burnt using a formula based on Metabolic Equivalent.
|
[
"['Mayur Sonawane' 'Sahil Rajesh Dhayalkar' 'Siddesh Waje'\n 'Soyal Markhelkar' 'Akshay Wattamwar' 'Seema C. Shrawne']"
] |
null | null |
2404.02873
| null | null |
http://arxiv.org/pdf/2404.02873v1
|
2024-04-03T17:09:25Z
|
2024-04-03T17:09:25Z
|
Gaussian Process Regression with Soft Inequality and Monotonicity
Constraints
|
Gaussian process (GP) regression is a non-parametric, Bayesian framework to approximate complex models. Standard GP regression can lead to an unbounded model in which some points can take infeasible values. We introduce a new GP method that enforces the physical constraints in a probabilistic manner. This GP model is trained by the quantum-inspired Hamiltonian Monte Carlo (QHMC). QHMC is an efficient way to sample from a broad class of distributions. Unlike the standard Hamiltonian Monte Carlo algorithm in which a particle has a fixed mass, QHMC allows a particle to have a random mass matrix with a probability distribution. Introducing the QHMC method to the inequality and monotonicity constrained GP regression in the probabilistic sense, our approach improves the accuracy and reduces the variance in the resulting GP model. According to our experiments on several datasets, the proposed approach serves as an efficient method as it accelerates the sampling process while maintaining the accuracy, and it is applicable to high dimensional problems.
|
[
"['Didem Kochan' 'Xiu Yang']"
] |
null | null |
2404.02882
| null | null |
http://arxiv.org/pdf/2404.02882v1
|
2024-04-03T17:33:21Z
|
2024-04-03T17:33:21Z
|
Linear Attention Sequence Parallelism
|
Sequence Parallel (SP) serves as a prevalent strategy to handle long sequences that exceed the memory limit of a single GPU. However, existing SP methods do not take advantage of linear attention features, resulting in sub-optimal parallelism efficiency and usability for linear attention-based language models. In this paper, we introduce Linear Attention Sequence Parallel (LASP), an efficient SP method tailored to linear attention-based language models. Specifically, we design an efficient point-to-point communication mechanism to leverage the right-product kernel trick of linear attention, which sharply decreases the communication overhead of SP. We also enhance the practical efficiency of LASP by performing kernel fusion and intermediate state caching, making the implementation of LASP hardware-friendly on GPU clusters. Furthermore, we meticulously ensure the compatibility of sequence-level LASP with all types of batch-level data parallel methods, which is vital for distributed training on large clusters with long sequences and large batches. We conduct extensive experiments on two linear attention-based models with varying sequence lengths and GPU cluster sizes. LASP scales sequence length up to 4096K using 128 A100 80G GPUs on 1B models, which is 8 times longer than existing SP methods while being significantly faster. The code is available at https://github.com/OpenNLPLab/LASP.
|
[
"['Weigao Sun' 'Zhen Qin' 'Dong Li' 'Xuyang Shen' 'Yu Qiao' 'Yiran Zhong']"
] |
null | null |
2404.02883
| null | null |
http://arxiv.org/pdf/2404.02883v1
|
2024-04-03T17:34:28Z
|
2024-04-03T17:34:28Z
|
On the Scalability of Diffusion-based Text-to-Image Generation
|
Scaling up model and data size has been quite successful for the evolution of LLMs. However, the scaling law for the diffusion based text-to-image (T2I) models is not fully explored. It is also unclear how to efficiently scale the model for better performance at reduced cost. The different training settings and expensive training cost make a fair model comparison extremely difficult. In this work, we empirically study the scaling properties of diffusion based T2I models by performing extensive and rigours ablations on scaling both denoising backbones and training set, including training scaled UNet and Transformer variants ranging from 0.4B to 4B parameters on datasets upto 600M images. For model scaling, we find the location and amount of cross attention distinguishes the performance of existing UNet designs. And increasing the transformer blocks is more parameter-efficient for improving text-image alignment than increasing channel numbers. We then identify an efficient UNet variant, which is 45% smaller and 28% faster than SDXL's UNet. On the data scaling side, we show the quality and diversity of the training set matters more than simply dataset size. Increasing caption density and diversity improves text-image alignment performance and the learning efficiency. Finally, we provide scaling functions to predict the text-image alignment performance as functions of the scale of model size, compute and dataset size.
|
[
"['Hao Li' 'Yang Zou' 'Ying Wang' 'Orchid Majumder' 'Yusheng Xie'\n 'R. Manmatha' 'Ashwin Swaminathan' 'Zhuowen Tu' 'Stefano Ermon'\n 'Stefano Soatto']"
] |
null | null |
2404.02892
| null | null |
http://arxiv.org/pdf/2404.02892v2
|
2024-04-07T01:02:08Z
|
2024-04-03T17:49:41Z
|
MODNO: Multi Operator Learning With Distributed Neural Operators
|
The study of operator learning involves the utilization of neural networks to approximate operators. Traditionally, the focus has been on single-operator learning (SOL). However, recent advances have rapidly expanded this to include the approximation of multiple operators using foundation models equipped with millions or billions of trainable parameters, leading to the research of multi-operator learning (MOL). In this paper, we present a novel distributed training approach aimed at enabling a single neural operator with significantly fewer parameters to effectively tackle multi-operator learning challenges, all without incurring additional average costs. Our method is applicable to various neural operators, such as Deep Operator Neural Networks (DON). The core idea is to independently learn the output basis functions for each operator using its dedicated data, while simultaneously centralizing the learning of the input function encoding shared by all operators using the entire dataset. Through a systematic study of five numerical examples, we compare the accuracy and cost of training a single neural operator for each operator independently versus training a MOL model using our proposed method. Our results demonstrate enhanced efficiency and satisfactory accuracy. Moreover, our approach illustrates that some operators with limited data can be more effectively constructed with the aid of data from analogous operators through MOL learning. This highlights another MOL's potential to bolster operator learning.
|
[
"['Zecheng Zhang']"
] |
null | null |
2404.02896
| null | null |
http://arxiv.org/pdf/2404.02896v1
|
2024-04-03T17:53:32Z
|
2024-04-03T17:53:32Z
|
Comment on "Machine learning conservation laws from differential
equations"
|
In lieu of abstract, first paragraph reads: Six months after the author derived a constant of motion for a 1D damped harmonic oscillator [1], a similar result appeared by Liu, Madhavan, and Tegmark [2, 3], without citing the author. However, their derivation contained six serious errors, causing both their method and result to be incorrect. In this Comment, those errors are reviewed.
|
[
"['Michael F. Zimmer']"
] |
null | null |
2404.02900
| null | null |
http://arxiv.org/pdf/2404.02900v1
|
2024-04-03T17:58:21Z
|
2024-04-03T17:58:21Z
|
DeiT-LT Distillation Strikes Back for Vision Transformer Training on
Long-Tailed Datasets
|
Vision Transformer (ViT) has emerged as a prominent architecture for various computer vision tasks. In ViT, we divide the input image into patch tokens and process them through a stack of self attention blocks. However, unlike Convolutional Neural Networks (CNN), ViTs simple architecture has no informative inductive bias (e.g., locality,etc. ). Due to this, ViT requires a large amount of data for pre-training. Various data efficient approaches (DeiT) have been proposed to train ViT on balanced datasets effectively. However, limited literature discusses the use of ViT for datasets with long-tailed imbalances. In this work, we introduce DeiT-LT to tackle the problem of training ViTs from scratch on long-tailed datasets. In DeiT-LT, we introduce an efficient and effective way of distillation from CNN via distillation DIST token by using out-of-distribution images and re-weighting the distillation loss to enhance focus on tail classes. This leads to the learning of local CNN-like features in early ViT blocks, improving generalization for tail classes. Further, to mitigate overfitting, we propose distilling from a flat CNN teacher, which leads to learning low-rank generalizable features for DIST tokens across all ViT blocks. With the proposed DeiT-LT scheme, the distillation DIST token becomes an expert on the tail classes, and the classifier CLS token becomes an expert on the head classes. The experts help to effectively learn features corresponding to both the majority and minority classes using a distinct set of tokens within the same ViT architecture. We show the effectiveness of DeiT-LT for training ViT from scratch on datasets ranging from small-scale CIFAR-10 LT to large-scale iNaturalist-2018.
|
[
"['Harsh Rangwani' 'Pradipto Mondal' 'Mayank Mishra'\n 'Ashish Ramayee Asokan' 'R. Venkatesh Babu']"
] |
null | null |
2404.02904
| null | null |
http://arxiv.org/pdf/2404.02904v1
|
2024-04-03T17:59:36Z
|
2024-04-03T17:59:36Z
|
ALOHa: A New Measure for Hallucination in Captioning Models
|
Despite recent advances in multimodal pre-training for visual description, state-of-the-art models still produce captions containing errors, such as hallucinating objects not present in a scene. The existing prominent metric for object hallucination, CHAIR, is limited to a fixed set of MS COCO objects and synonyms. In this work, we propose a modernized open-vocabulary metric, ALOHa, which leverages large language models (LLMs) to measure object hallucinations. Specifically, we use an LLM to extract groundable objects from a candidate caption, measure their semantic similarity to reference objects from captions and object detections, and use Hungarian matching to produce a final hallucination score. We show that ALOHa correctly identifies 13.6% more hallucinated objects than CHAIR on HAT, a new gold-standard subset of MS COCO Captions annotated for hallucinations, and 30.8% more on nocaps, where objects extend beyond MS COCO categories. Our code is available at https://davidmchan.github.io/aloha/.
|
[
"['Suzanne Petryk' 'David M. Chan' 'Anish Kachinthaya' 'Haodi Zou'\n 'John Canny' 'Joseph E. Gonzalez' 'Trevor Darrell']"
] |
null | null |
2404.02923
| null | null |
http://arxiv.org/pdf/2404.02923v1
|
2024-03-31T01:20:01Z
|
2024-03-31T01:20:01Z
|
An Unsupervised Adversarial Autoencoder for Cyber Attack Detection in
Power Distribution Grids
|
Detection of cyber attacks in smart power distribution grids with unbalanced configurations poses challenges due to the inherent nonlinear nature of these uncertain and stochastic systems. It originates from the intermittent characteristics of the distributed energy resources (DERs) generation and load variations. Moreover, the unknown behavior of cyber attacks, especially false data injection attacks (FDIAs) in the distribution grids with complex temporal correlations and the limited amount of labeled data increases the vulnerability of the grids and imposes a high risk in the secure and reliable operation of the grids. To address these challenges, this paper proposes an unsupervised adversarial autoencoder (AAE) model to detect FDIAs in unbalanced power distribution grids integrated with DERs, i.e., PV systems and wind generation. The proposed method utilizes long short-term memory (LSTM) in the structure of the autoencoder to capture the temporal dependencies in the time-series measurements and leverages the power of generative adversarial networks (GANs) for better reconstruction of the input data. The advantage of the proposed data-driven model is that it can detect anomalous points for the system operation without reliance on abstract models or mathematical representations. To evaluate the efficacy of the approach, it is tested on IEEE 13-bus and 123-bus systems with historical meteorological data (wind speed, ambient temperature, and solar irradiance) as well as historical real-world load data under three types of data falsification functions. The comparison of the detection results of the proposed model with other unsupervised learning methods verifies its superior performance in detecting cyber attacks in unbalanced power distribution grids.
|
[
"['Mehdi Jabbari Zideh' 'Mohammad Reza Khalghani'\n 'Sarika Khushalani Solanki']"
] |
null | null |
2404.02926
| null | null |
http://arxiv.org/pdf/2404.02926v1
|
2024-04-01T23:09:52Z
|
2024-04-01T23:09:52Z
|
A High Order Solver for Signature Kernels
|
Signature kernels are at the core of several machine learning algorithms for analysing multivariate time series. The kernel of two bounded variation paths (such as piecewise linear interpolations of time series data) is typically computed by solving a Goursat problem for a hyperbolic partial differential equation (PDE) in two independent time variables. However, this approach becomes considerably less practical for highly oscillatory input paths, as they have to be resolved at a fine enough scale to accurately recover their signature kernel, resulting in significant time and memory complexities. To mitigate this issue, we first show that the signature kernel of a broader class of paths, known as emph{smooth rough paths}, also satisfies a PDE, albeit in the form of a system of coupled equations. We then use this result to introduce new algorithms for the numerical approximation of signature kernels. As bounded variation paths (and more generally geometric $p$-rough paths) can be approximated by piecewise smooth rough paths, one can replace the PDE with rapidly varying coefficients in the original Goursat problem by an explicit system of coupled equations with piecewise constant coefficients derived from the first few iterated integrals of the original input paths. While this approach requires solving more equations, they do not require looking back at the complex and fine structure of the initial paths, which significantly reduces the computational complexity associated with the analysis of highly oscillatory time series.
|
[
"['Maud Lemercier' 'Terry Lyons']"
] |
null | null |
2404.02934
| null | null |
http://arxiv.org/pdf/2404.02934v1
|
2024-04-03T02:16:37Z
|
2024-04-03T02:16:37Z
|
GreedLlama: Performance of Financial Value-Aligned Large Language Models
in Moral Reasoning
|
This paper investigates the ethical implications of aligning Large Language Models (LLMs) with financial optimization, through the case study of GreedLlama, a model fine-tuned to prioritize economically beneficial outcomes. By comparing GreedLlama's performance in moral reasoning tasks to a base Llama2 model, our results highlight a concerning trend: GreedLlama demonstrates a marked preference for profit over ethical considerations, making morally appropriate decisions at significantly lower rates than the base model in scenarios of both low and high moral ambiguity. In low ambiguity situations, GreedLlama's ethical decisions decreased to 54.4%, compared to the base model's 86.9%, while in high ambiguity contexts, the rate was 47.4% against the base model's 65.1%. These findings emphasize the risks of single-dimensional value alignment in LLMs, underscoring the need for integrating broader ethical values into AI development to ensure decisions are not solely driven by financial incentives. The study calls for a balanced approach to LLM deployment, advocating for the incorporation of ethical considerations in models intended for business applications, particularly in light of the absence of regulatory oversight.
|
[
"['Jeffy Yu' 'Maximilian Huber' 'Kevin Tang']"
] |
null | null |
2404.02935
| null | null |
http://arxiv.org/pdf/2404.02935v1
|
2024-04-03T02:52:07Z
|
2024-04-03T02:52:07Z
|
KnowHalu: Hallucination Detection via Multi-Form Knowledge Based Factual
Checking
|
This paper introduces KnowHalu, a novel approach for detecting hallucinations in text generated by large language models (LLMs), utilizing step-wise reasoning, multi-formulation query, multi-form knowledge for factual checking, and fusion-based detection mechanism. As LLMs are increasingly applied across various domains, ensuring that their outputs are not hallucinated is critical. Recognizing the limitations of existing approaches that either rely on the self-consistency check of LLMs or perform post-hoc fact-checking without considering the complexity of queries or the form of knowledge, KnowHalu proposes a two-phase process for hallucination detection. In the first phase, it identifies non-fabrication hallucinations--responses that, while factually correct, are irrelevant or non-specific to the query. The second phase, multi-form based factual checking, contains five key steps: reasoning and query decomposition, knowledge retrieval, knowledge optimization, judgment generation, and judgment aggregation. Our extensive evaluations demonstrate that KnowHalu significantly outperforms SOTA baselines in detecting hallucinations across diverse tasks, e.g., improving by 15.65% in QA tasks and 5.50% in summarization tasks, highlighting its effectiveness and versatility in detecting hallucinations in LLM-generated content.
|
[
"['Jiawei Zhang' 'Chejian Xu' 'Yu Gai' 'Freddy Lecue' 'Dawn Song' 'Bo Li']"
] |
null | null |
2404.02936
| null | null |
http://arxiv.org/pdf/2404.02936v3
|
2024-05-23T23:06:49Z
|
2024-04-03T04:25:01Z
|
Min-K%++: Improved Baseline for Detecting Pre-Training Data from Large
Language Models
|
The problem of pre-training data detection for large language models (LLMs) has received growing attention due to its implications in critical issues like copyright violation and test data contamination. Despite improved performance, existing methods (including the state-of-the-art, Min-K%) are mostly developed upon simple heuristics and lack solid, reasonable foundations. In this work, we propose a novel and theoretically motivated methodology for pre-training data detection, named Min-K%++. Specifically, we present a key insight that training samples tend to be local maxima of the modeled distribution along each input dimension through maximum likelihood training, which in turn allow us to insightfully translate the problem into identification of local maxima. Then, we design our method accordingly that works under the discrete distribution modeled by LLMs, whose core idea is to determine whether the input forms a mode or has relatively high probability under the conditional categorical distribution. Empirically, the proposed method achieves new SOTA performance across multiple settings. On the WikiMIA benchmark, Min-K%++ outperforms the runner-up by 6.2% to 10.5% in detection AUROC averaged over five models. On the more challenging MIMIR benchmark, it consistently improves upon reference-free methods while performing on par with reference-based method that requires an extra reference model.
|
[
"['Jingyang Zhang' 'Jingwei Sun' 'Eric Yeats' 'Yang Ouyang' 'Martin Kuo'\n 'Jianyi Zhang' 'Hao Frank Yang' 'Hai Li']"
] |
null | null |
2404.02937
| null | null |
http://arxiv.org/pdf/2404.02937v4
|
2024-04-21T15:37:31Z
|
2024-04-03T07:14:15Z
|
Towards Responsible and Reliable Traffic Flow Prediction with Large
Language Models
|
Traffic forecasting is crucial for intelligent transportation systems. It has experienced significant advancements thanks to the power of deep learning in capturing latent patterns of traffic data. However, recent deep-learning architectures require intricate model designs and lack an intuitive understanding of the mapping from input data to predicted results. Achieving both accuracy and responsibility in traffic prediction models remains a challenge due to the complexity of traffic data and the inherent opacity of deep learning models. To tackle these challenges, we propose a Responsible and Reliable Traffic flow forecasting model with Large Language Models (R2T-LLM), which leverages large language models (LLMs) to generate responsible traffic predictions. By transferring multi-modal traffic data into natural language descriptions, R2T-LLM captures complex spatial-temporal patterns and external factors from comprehensive traffic data. The LLM framework is fine-tuned using language-based instructions to align with spatial-temporal traffic flow data. Empirically, R2T-LLM shows competitive accuracy compared with deep learning baselines, while providing an intuitive and reliable explanation for predictions. We discuss the spatial-temporal and input dependencies for conditional future flow forecasting, showcasing R2T-LLM's potential for diverse city prediction tasks. This paper contributes to advancing accountable traffic prediction models and lays a foundation for future exploration of LLM applications in transportation. To the best of our knowledge, this is the first study to use LLM for accountable and reliable prediction of traffic flows.
|
[
"['Xusen Guo' 'Qiming Zhang' 'Junyue Jiang' 'Mingxing Peng' 'Hao' 'Yang'\n 'Meixin Zhu']"
] |
null | null |
2404.02942
| null | null |
http://arxiv.org/pdf/2404.02942v1
|
2024-04-03T12:38:12Z
|
2024-04-03T12:38:12Z
|
Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles
|
Understanding the decisions of tree-based ensembles and their relationships is pivotal for machine learning model interpretation. Recent attempts to mitigate the human-in-the-loop interpretation challenge have explored the extraction of the decision structure underlying the model taking advantage of graph simplification and path emphasis. However, while these efforts enhance the visualisation experience, they may either result in a visually complex representation or compromise the interpretability of the original ensemble model. In addressing this challenge, especially in complex scenarios, we introduce the Decision Predicate Graph (DPG) as a model-agnostic tool to provide a global interpretation of the model. DPG is a graph structure that captures the tree-based ensemble model and learned dataset details, preserving the relations among features, logical decisions, and predictions towards emphasising insightful points. Leveraging well-known graph theory concepts, such as the notions of centrality and community, DPG offers additional quantitative insights into the model, complementing visualisation techniques, expanding the problem space descriptions, and offering diverse possibilities for extensions. Empirical experiments demonstrate the potential of DPG in addressing traditional benchmarks and complex classification scenarios.
|
[
"['Leonardo Arrighi' 'Luca Pennella' 'Gabriel Marques Tavares'\n 'Sylvio Barbon Junior']"
] |
null | null |
2404.02943
| null | null |
http://arxiv.org/abs/2404.02943v1
|
2024-04-03T13:31:49Z
|
2024-04-03T13:31:49Z
|
Learning in Convolutional Neural Networks Accelerated by Transfer
Entropy
|
Recently, there is a growing interest in applying Transfer Entropy (TE) in quantifying the effective connectivity between artificial neurons. In a feedforward network, the TE can be used to quantify the relationships between neuron output pairs located in different layers. Our focus is on how to include the TE in the learning mechanisms of a Convolutional Neural Network (CNN) architecture. We introduce a novel training mechanism for CNN architectures which integrates the TE feedback connections. Adding the TE feedback parameter accelerates the training process, as fewer epochs are needed. On the flip side, it adds computational overhead to each epoch. According to our experiments on CNN classifiers, to achieve a reasonable computational overhead--accuracy trade-off, it is efficient to consider only the inter-neural information transfer of a random subset of the neuron pairs from the last two fully connected layers. The TE acts as a smoothing factor, generating stability and becoming active only periodically, not after processing each input sample. Therefore, we can consider the TE is in our model a slowly changing meta-parameter.
|
[
"['Adrian Moldovan' 'Angel Caţaron' 'Răzvan Andonie']"
] |
null | null |
2404.02944
| null | null |
http://arxiv.org/pdf/2404.02944v1
|
2024-04-03T13:32:44Z
|
2024-04-03T13:32:44Z
|
Foundation Models for Structural Health Monitoring
|
Structural Health Monitoring (SHM) is a critical task for ensuring the safety and reliability of civil infrastructures, typically realized on bridges and viaducts by means of vibration monitoring. In this paper, we propose for the first time the use of Transformer neural networks, with a Masked Auto-Encoder architecture, as Foundation Models for SHM. We demonstrate the ability of these models to learn generalizable representations from multiple large datasets through self-supervised pre-training, which, coupled with task-specific fine-tuning, allows them to outperform state-of-the-art traditional methods on diverse tasks, including Anomaly Detection (AD) and Traffic Load Estimation (TLE). We then extensively explore model size versus accuracy trade-offs and experiment with Knowledge Distillation (KD) to improve the performance of smaller Transformers, enabling their embedding directly into the SHM edge nodes. We showcase the effectiveness of our foundation models using data from three operational viaducts. For AD, we achieve a near-perfect 99.9% accuracy with a monitoring time span of just 15 windows. In contrast, a state-of-the-art method based on Principal Component Analysis (PCA) obtains its first good result (95.03% accuracy) only considering 120 windows. On two different TLE tasks, our models obtain state-of-the-art performance on multiple evaluation metrics (R$^2$ score, MAE% and MSE%). On the first benchmark, we achieve an R$^2$ score of 0.97 and 0.85 for light and heavy vehicle traffic, respectively, while the best previous approach stops at 0.91 and 0.84. On the second one, we achieve an R$^2$ score of 0.54 versus the 0.10 of the best existing method.
|
[
"['Luca Benfenati' 'Daniele Jahier Pagliari' 'Luca Zanatta'\n 'Yhorman Alexander Bedoya Velez' 'Andrea Acquaviva' 'Massimo Poncino'\n 'Enrico Macii' 'Luca Benini' 'Alessio Burrello']"
] |
null | null |
2404.02945
| null | null |
http://arxiv.org/pdf/2404.02945v1
|
2024-04-03T14:14:08Z
|
2024-04-03T14:14:08Z
|
Optimizing the Deployment of Tiny Transformers on Low-Power MCUs
|
Transformer networks are rapidly becoming SotA in many fields, such as NLP and CV. Similarly to CNN, there is a strong push for deploying Transformer models at the extreme edge, ultimately fitting the tiny power budget and memory footprint of MCUs. However, the early approaches in this direction are mostly ad-hoc, platform, and model-specific. This work aims to enable and optimize the flexible, multi-platform deployment of encoder Tiny Transformers on commercial MCUs. We propose a complete framework to perform end-to-end deployment of Transformer models onto single and multi-core MCUs. Our framework provides an optimized library of kernels to maximize data reuse and avoid unnecessary data marshaling operations into the crucial attention block. A novel MHSA inference schedule, named Fused-Weight Self-Attention, is introduced, fusing the linear projection weights offline to further reduce the number of operations and parameters. Furthermore, to mitigate the memory peak reached by the computation of the attention map, we present a Depth-First Tiling scheme for MHSA. We evaluate our framework on three different MCU classes exploiting ARM and RISC-V ISA, namely the STM32H7, the STM32L4, and GAP9 (RV32IMC-XpulpV2). We reach an average of 4.79x and 2.0x lower latency compared to SotA libraries CMSIS-NN (ARM) and PULP-NN (RISC-V), respectively. Moreover, we show that our MHSA depth-first tiling scheme reduces the memory peak by up to 6.19x, while the fused-weight attention can reduce the runtime by 1.53x, and number of parameters by 25%. We report significant improvements across several Tiny Transformers: for instance, when executing a transformer block for the task of radar-based hand-gesture recognition on GAP9, we achieve a latency of 0.14ms and energy consumption of 4.92 micro-joules, 2.32x lower than the SotA PULP-NN library on the same platform.
|
[
"['Victor J. B. Jung' 'Alessio Burrello' 'Moritz Scherer' 'Francesco Conti'\n 'Luca Benini']"
] |
null | null |
2404.02947
| null | null |
http://arxiv.org/pdf/2404.02947v1
|
2024-04-03T15:06:09Z
|
2024-04-03T15:06:09Z
|
DNN Memory Footprint Reduction via Post-Training Intra-Layer
Multi-Precision Quantization
|
The imperative to deploy Deep Neural Network (DNN) models on resource-constrained edge devices, spurred by privacy concerns, has become increasingly apparent. To facilitate the transition from cloud to edge computing, this paper introduces a technique that effectively reduces the memory footprint of DNNs, accommodating the limitations of resource-constrained edge devices while preserving model accuracy. Our proposed technique, named Post-Training Intra-Layer Multi-Precision Quantization (PTILMPQ), employs a post-training quantization approach, eliminating the need for extensive training data. By estimating the importance of layers and channels within the network, the proposed method enables precise bit allocation throughout the quantization process. Experimental results demonstrate that PTILMPQ offers a promising solution for deploying DNNs on edge devices with restricted memory resources. For instance, in the case of ResNet50, it achieves an accuracy of 74.57% with a memory footprint of 9.5 MB, representing a 25.49% reduction compared to previous similar methods, with only a minor 1.08% decrease in accuracy.
|
[
"['Behnam Ghavami' 'Amin Kamjoo' 'Lesley Shannon' 'Steve Wilton']"
] |
null | null |
2404.02948
| null | null |
http://arxiv.org/pdf/2404.02948v3
|
2024-05-28T14:19:33Z
|
2024-04-03T15:06:43Z
|
PiSSA: Principal Singular Values and Singular Vectors Adaptation of
Large Language Models
|
To parameter-efficiently fine-tune (PEFT) large language models (LLMs), the low-rank adaptation (LoRA) method approximates the model changes $Delta W in mathbb{R}^{m times n}$ through the product of two matrices $A in mathbb{R}^{m times r}$ and $B in mathbb{R}^{r times n}$, where $r ll min(m, n)$, $A$ is initialized with Gaussian noise, and $B$ with zeros. LoRA freezes the original model $W$ and updates the "Noise & Zero" adapter, which may lead to slow convergence. To overcome this limitation, we introduce Principal Singular values and Singular vectors Adaptation (PiSSA). PiSSA shares the same architecture as LoRA, but initializes the adaptor matrices $A$ and $B$ with the principal components of the original matrix $W$, and put the remaining components into a residual matrix $W^{res} in mathbb{R}^{m times n}$ which is frozen during fine-tuning. Compared to LoRA, PiSSA updates the principal components while freezing the "residual" parts, allowing faster convergence and enhanced performance. Comparative experiments of PiSSA and LoRA across 12 different models, ranging from 184M to 70B, encompassing 5 NLG and 8 NLU tasks, reveal that PiSSA consistently outperforms LoRA under identical experimental setups. On the GSM8K benchmark, Mistral-7B fine-tuned with PiSSA achieves an accuracy of 72.86%, surpassing LoRA's 67.7% by 5.16%. Due to the same architecture, PiSSA is also compatible with quantization to further reduce the memory requirement of fine-tuning. Compared to QLoRA, QPiSSA (PiSSA with 4-bit quantization) exhibits smaller quantization errors in the initial stages. Fine-tuning LLaMA-3-70B on GSM8K, QPiSSA attains an accuracy of 86.05%, exceeding the performances of QLoRA at 81.73%. Leveraging a fast SVD technique, PiSSA can be initialized in only a few seconds, presenting a negligible cost for transitioning from LoRA to PiSSA.
|
[
"['Fanxu Meng' 'Zhaohui Wang' 'Muhan Zhang']"
] |
null | null |
2404.02949
| null | null |
http://arxiv.org/pdf/2404.02949v1
|
2024-04-03T17:56:28Z
|
2024-04-03T17:56:28Z
|
The SaTML '24 CNN Interpretability Competition: New Innovations for
Concept-Level Interpretability
|
Interpretability techniques are valuable for helping humans understand and oversee AI systems. The SaTML 2024 CNN Interpretability Competition solicited novel methods for studying convolutional neural networks (CNNs) at the ImageNet scale. The objective of the competition was to help human crowd-workers identify trojans in CNNs. This report showcases the methods and results of four featured competition entries. It remains challenging to help humans reliably diagnose trojans via interpretability tools. However, the competition's entries have contributed new techniques and set a new record on the benchmark from Casper et al., 2023.
|
[
"['Stephen Casper' 'Jieun Yun' 'Joonhyuk Baek' 'Yeseong Jung' 'Minhwan Kim'\n 'Kiwan Kwon' 'Saerom Park' 'Hayden Moore' 'David Shriver'\n 'Marissa Connor' 'Keltin Grimes' 'Angus Nicolson' 'Arush Tagade'\n 'Jessica Rumbelow' 'Hieu Minh Nguyen' 'Dylan Hadfield-Menell']"
] |
null | null |
2404.02954
| null | null |
http://arxiv.org/pdf/2404.02954v1
|
2024-04-03T18:00:00Z
|
2024-04-03T18:00:00Z
|
Deep Generative Models through the Lens of the Manifold Hypothesis: A
Survey and New Connections
|
In recent years there has been increased interest in understanding the interplay between deep generative models (DGMs) and the manifold hypothesis. Research in this area focuses on understanding the reasons why commonly-used DGMs succeed or fail at learning distributions supported on unknown low-dimensional manifolds, as well as developing new models explicitly designed to account for manifold-supported data. This manifold lens provides both clarity as to why some DGMs (e.g. diffusion models and some generative adversarial networks) empirically surpass others (e.g. likelihood-based models such as variational autoencoders, normalizing flows, or energy-based models) at sample generation, and guidance for devising more performant DGMs. We carry out the first survey of DGMs viewed through this lens, making two novel contributions along the way. First, we formally establish that numerical instability of high-dimensional likelihoods is unavoidable when modelling low-dimensional data. We then show that DGMs on learned representations of autoencoders can be interpreted as approximately minimizing Wasserstein distance: this result, which applies to latent diffusion models, helps justify their outstanding empirical results. The manifold lens provides a rich perspective from which to understand DGMs, which we aim to make more accessible and widespread.
|
[
"['Gabriel Loaiza-Ganem' 'Brendan Leigh Ross' 'Rasa Hosseinzadeh'\n 'Anthony L. Caterini' 'Jesse C. Cresswell']"
] |
null | null |
2404.02986
| null | null |
http://arxiv.org/pdf/2404.02986v1
|
2024-04-03T18:14:23Z
|
2024-04-03T18:14:23Z
|
Universal Functional Regression with Neural Operator Flows
|
Regression on function spaces is typically limited to models with Gaussian process priors. We introduce the notion of universal functional regression, in which we aim to learn a prior distribution over non-Gaussian function spaces that remains mathematically tractable for functional regression. To do this, we develop Neural Operator Flows (OpFlow), an infinite-dimensional extension of normalizing flows. OpFlow is an invertible operator that maps the (potentially unknown) data function space into a Gaussian process, allowing for exact likelihood estimation of functional point evaluations. OpFlow enables robust and accurate uncertainty quantification via drawing posterior samples of the Gaussian process and subsequently mapping them into the data function space. We empirically study the performance of OpFlow on regression and generation tasks with data generated from Gaussian processes with known posterior forms and non-Gaussian processes, as well as real-world earthquake seismograms with an unknown closed-form distribution.
|
[
"['Yaozhong Shi' 'Angela F. Gao' 'Zachary E. Ross' 'Kamyar Azizzadenesheli']"
] |
null | null |
2404.02988
| null | null |
http://arxiv.org/pdf/2404.02988v1
|
2024-04-03T18:16:47Z
|
2024-04-03T18:16:47Z
|
Risk-averse Learning with Non-Stationary Distributions
|
Considering non-stationary environments in online optimization enables decision-maker to effectively adapt to changes and improve its performance over time. In such cases, it is favorable to adopt a strategy that minimizes the negative impact of change to avoid potentially risky situations. In this paper, we investigate risk-averse online optimization where the distribution of the random cost changes over time. We minimize risk-averse objective function using the Conditional Value at Risk (CVaR) as risk measure. Due to the difficulty in obtaining the exact CVaR gradient, we employ a zeroth-order optimization approach that queries the cost function values multiple times at each iteration and estimates the CVaR gradient using the sampled values. To facilitate the regret analysis, we use a variation metric based on Wasserstein distance to capture time-varying distributions. Given that the distribution variation is sub-linear in the total number of episodes, we show that our designed learning algorithm achieves sub-linear dynamic regret with high probability for both convex and strongly convex functions. Moreover, theoretical results suggest that increasing the number of samples leads to a reduction in the dynamic regret bounds until the sampling number reaches a specific limit. Finally, we provide numerical experiments of dynamic pricing in a parking lot to illustrate the efficacy of the designed algorithm.
|
[
"['Siyi Wang' 'Zifan Wang' 'Xinlei Yi' 'Michael M. Zavlanos'\n 'Karl H. Johansson' 'Sandra Hirche']"
] |
null | null |
2404.03010
| null | null |
http://arxiv.org/pdf/2404.03010v1
|
2024-04-03T18:42:19Z
|
2024-04-03T18:42:19Z
|
Skeleton Recall Loss for Connectivity Conserving and Resource Efficient
Segmentation of Thin Tubular Structures
|
Accurately segmenting thin tubular structures, such as vessels, nerves, roads or concrete cracks, is a crucial task in computer vision. Standard deep learning-based segmentation loss functions, such as Dice or Cross-Entropy, focus on volumetric overlap, often at the expense of preserving structural connectivity or topology. This can lead to segmentation errors that adversely affect downstream tasks, including flow calculation, navigation, and structural inspection. Although current topology-focused losses mark an improvement, they introduce significant computational and memory overheads. This is particularly relevant for 3D data, rendering these losses infeasible for larger volumes as well as increasingly important multi-class segmentation problems. To mitigate this, we propose a novel Skeleton Recall Loss, which effectively addresses these challenges by circumventing intensive GPU-based calculations with inexpensive CPU operations. It demonstrates overall superior performance to current state-of-the-art approaches on five public datasets for topology-preserving segmentation, while substantially reducing computational overheads by more than 90%. In doing so, we introduce the first multi-class capable loss function for thin structure segmentation, excelling in both efficiency and efficacy for topology-preservation.
|
[
"['Yannick Kirchhoff' 'Maximilian R. Rokuss' 'Saikat Roy' 'Balint Kovacs'\n 'Constantin Ulrich' 'Tassilo Wald' 'Maximilian Zenk' 'Philipp Vollmuth'\n 'Jens Kleesiek' 'Fabian Isensee' 'Klaus Maier-Hein']"
] |
null | null |
2404.03011
| null | null |
http://arxiv.org/abs/2404.03011v1
|
2024-04-03T18:48:45Z
|
2024-04-03T18:48:45Z
|
Transfer learning applications for anomaly detection in wind turbines
|
Anomaly detection in wind turbines typically involves using normal behaviour models to detect faults early. However, training autoencoder models for each turbine is time-consuming and resource intensive. Thus, transfer learning becomes essential for wind turbines with limited data or applications with limited computational resources. This study examines how cross-turbine transfer learning can be applied to autoencoder-based anomaly detection. Here, autoencoders are combined with constant thresholds for the reconstruction error to determine if input data contains an anomaly. The models are initially trained on one year's worth of data from one or more source wind turbines. They are then fine-tuned using smaller amounts of data from another turbine. Three methods for fine-tuning are investigated: adjusting the entire autoencoder, only the decoder, or only the threshold of the model. The performance of the transfer learning models is compared to baseline models that were trained on one year's worth of data from the target wind turbine. The results of the tests conducted in this study indicate that models trained on data of multiple wind turbines do not improve the anomaly detection capability compared to models trained on data of one source wind turbine. In addition, modifying the model's threshold can lead to comparable or even superior performance compared to the baseline, whereas fine-tuning the decoder or autoencoder further enhances the models' performance.
|
[
"['Cyriana M. A. Roelofs' 'Christian Gück' 'Stefan Faulstich']"
] |
null | null |
2404.03012
| null | null |
http://arxiv.org/pdf/2404.03012v1
|
2024-04-03T18:50:14Z
|
2024-04-03T18:50:14Z
|
Spectral Clustering in Convex and Constrained Settings
|
Spectral clustering methods have gained widespread recognition for their effectiveness in clustering high-dimensional data. Among these techniques, constrained spectral clustering has emerged as a prominent approach, demonstrating enhanced performance by integrating pairwise constraints. However, the application of such constraints to semidefinite spectral clustering, a variant that leverages semidefinite programming to optimize clustering objectives, remains largely unexplored. In this paper, we introduce a novel framework for seamlessly integrating pairwise constraints into semidefinite spectral clustering. Our methodology systematically extends the capabilities of semidefinite spectral clustering to capture complex data structures, thereby addressing real-world clustering challenges more effectively. Additionally, we extend this framework to encompass both active and self-taught learning scenarios, further enhancing its versatility and applicability. Empirical studies conducted on well-known datasets demonstrate the superiority of our proposed framework over existing spectral clustering methods, showcasing its robustness and scalability across diverse datasets and learning settings. By bridging the gap between constrained learning and semidefinite spectral clustering, our work contributes to the advancement of spectral clustering techniques, offering researchers and practitioners a versatile tool for addressing complex clustering challenges in various real-world applications. Access to the data, code, and experimental results is provided for further exploration (https://github.com/swarupbehera/SCCCS).
|
[
"['Swarup Ranjan Behera' 'Vijaya V. Saradhi']"
] |
null | null |
2404.03017
| null | null |
http://arxiv.org/pdf/2404.03017v1
|
2024-04-03T18:57:54Z
|
2024-04-03T18:57:54Z
|
Distributionally Robust Policy and Lyapunov-Certificate Learning
|
This article presents novel methods for synthesizing distributionally robust stabilizing neural controllers and certificates for control systems under model uncertainty. A key challenge in designing controllers with stability guarantees for uncertain systems is the accurate determination of and adaptation to shifts in model parametric uncertainty during online deployment. We tackle this with a novel distributionally robust formulation of the Lyapunov derivative chance constraint ensuring a monotonic decrease of the Lyapunov certificate. To avoid the computational complexity involved in dealing with the space of probability measures, we identify a sufficient condition in the form of deterministic convex constraints that ensures the Lyapunov derivative constraint is satisfied. We integrate this condition into a loss function for training a neural network-based controller and show that, for the resulting closed-loop system, the global asymptotic stability of its equilibrium can be certified with high confidence, even with Out-of-Distribution (OoD) model uncertainties. To demonstrate the efficacy and efficiency of the proposed methodology, we compare it with an uncertainty-agnostic baseline approach and several reinforcement learning approaches in two control problems in simulation.
|
[
"['Kehan Long' 'Jorge Cortes' 'Nikolay Atanasov']"
] |
null | null |
2404.03019
| null | null |
http://arxiv.org/pdf/2404.03019v2
|
2024-04-08T01:06:38Z
|
2024-04-03T19:03:15Z
|
GeoT: Tensor Centric Library for Graph Neural Network via Efficient
Segment Reduction on GPU
|
In recent years, Graph Neural Networks (GNNs) have ignited a surge of innovation, significantly enhancing the processing of geometric data structures such as graphs, point clouds, and meshes. As the domain continues to evolve, a series of frameworks and libraries are being developed to push GNN efficiency to new heights. While graph-centric libraries have achieved success in the past, the advent of efficient tensor compilers has highlighted the urgent need for tensor-centric libraries. Yet, efficient tensor-centric frameworks for GNNs remain scarce due to unique challenges and limitations encountered when implementing segment reduction in GNN contexts. We introduce GeoT, a cutting-edge tensor-centric library designed specifically for GNNs via efficient segment reduction. GeoT debuts innovative parallel algorithms that not only introduce new design principles but also expand the available design space. Importantly, GeoT is engineered for straightforward fusion within a computation graph, ensuring compatibility with contemporary tensor-centric machine learning frameworks and compilers. Setting a new performance benchmark, GeoT marks a considerable advancement by showcasing an average operator speedup of 1.80x and an end-to-end speedup of 1.68x.
|
[
"['Zhongming Yu' 'Genghan Zhang' 'Hanxian Huang' 'Xin Chen' 'Jishen Zhao']"
] |
null | null |
2404.03022
| null | null |
http://arxiv.org/pdf/2404.03022v2
|
2024-06-11T19:34:19Z
|
2024-04-03T19:17:43Z
|
BCAmirs at SemEval-2024 Task 4: Beyond Words: A Multimodal and
Multilingual Exploration of Persuasion in Memes
|
Memes, combining text and images, frequently use metaphors to convey persuasive messages, shaping public opinion. Motivated by this, our team engaged in SemEval-2024 Task 4, a hierarchical multi-label classification task designed to identify rhetorical and psychological persuasion techniques embedded within memes. To tackle this problem, we introduced a caption generation step to assess the modality gap and the impact of additional semantic information from images, which improved our result. Our best model utilizes GPT-4 generated captions alongside meme text to fine-tune RoBERTa as the text encoder and CLIP as the image encoder. It outperforms the baseline by a large margin in all 12 subtasks. In particular, it ranked in top-3 across all languages in Subtask 2a, and top-4 in Subtask 2b, demonstrating quantitatively strong performance. The improvement achieved by the introduced intermediate step is likely attributable to the metaphorical essence of images that challenges visual encoders. This highlights the potential for improving abstract visual semantics encoding.
|
[
"['Amirhossein Abaskohi' 'Amirhossein Dabiriaghdam' 'Lele Wang'\n 'Giuseppe Carenini']"
] |
null | null |
2404.03037
| null | null |
http://arxiv.org/pdf/2404.03037v3
|
2024-05-24T02:15:42Z
|
2024-04-03T19:48:13Z
|
Model-based Reinforcement Learning for Parameterized Action Spaces
|
We propose a novel model-based reinforcement learning algorithm -- Dynamics Learning and predictive control with Parameterized Actions (DLPA) -- for Parameterized Action Markov Decision Processes (PAMDPs). The agent learns a parameterized-action-conditioned dynamics model and plans with a modified Model Predictive Path Integral control. We theoretically quantify the difference between the generated trajectory and the optimal trajectory during planning in terms of the value they achieved through the lens of Lipschitz Continuity. Our empirical results on several standard benchmarks show that our algorithm achieves superior sample efficiency and asymptotic performance than state-of-the-art PAMDP methods.
|
[
"['Renhao Zhang' 'Haotian Fu' 'Yilin Miao' 'George Konidaris']"
] |
null | null |
2404.03044
| null | null |
http://arxiv.org/pdf/2404.03044v1
|
2024-04-03T20:08:15Z
|
2024-04-03T20:08:15Z
|
The Artificial Intelligence Ontology: LLM-assisted construction of AI
concept hierarchies
|
The Artificial Intelligence Ontology (AIO) is a systematization of artificial intelligence (AI) concepts, methodologies, and their interrelations. Developed via manual curation, with the additional assistance of large language models (LLMs), AIO aims to address the rapidly evolving landscape of AI by providing a comprehensive framework that encompasses both technical and ethical aspects of AI technologies. The primary audience for AIO includes AI researchers, developers, and educators seeking standardized terminology and concepts within the AI domain. The ontology is structured around six top-level branches: Networks, Layers, Functions, LLMs, Preprocessing, and Bias, each designed to support the modular composition of AI methods and facilitate a deeper understanding of deep learning architectures and ethical considerations in AI. AIO's development utilized the Ontology Development Kit (ODK) for its creation and maintenance, with its content being dynamically updated through AI-driven curation support. This approach not only ensures the ontology's relevance amidst the fast-paced advancements in AI but also significantly enhances its utility for researchers, developers, and educators by simplifying the integration of new AI concepts and methodologies. The ontology's utility is demonstrated through the annotation of AI methods data in a catalog of AI research publications and the integration into the BioPortal ontology resource, highlighting its potential for cross-disciplinary research. The AIO ontology is open source and is available on GitHub (https://github.com/berkeleybop/artificial-intelligence-ontology) and BioPortal (https://bioportal.bioontology.org/ontologies/AIO).
|
[
"['Marcin P. Joachimiak' 'Mark A. Miller' 'J. Harry Caufield' 'Ryan Ly'\n 'Nomi L. Harris' 'Andrew Tritt' 'Christopher J. Mungall'\n 'Kristofer E. Bouchard']"
] |
null | null |
2404.03050
| null | null |
http://arxiv.org/pdf/2404.03050v1
|
2024-04-03T20:34:18Z
|
2024-04-03T20:34:18Z
|
ANOVA-boosting for Random Fourier Features
|
We propose two algorithms for boosting random Fourier feature models for approximating high-dimensional functions. These methods utilize the classical and generalized analysis of variance (ANOVA) decomposition to learn low-order functions, where there are few interactions between the variables. Our algorithms are able to find an index set of important input variables and variable interactions reliably. Furthermore, we generalize already existing random Fourier feature models to an ANOVA setting, where terms of different order can be used. Our algorithms have the advantage of interpretability, meaning that the influence of every input variable is known in the learned model, even for dependent input variables. We give theoretical as well as numerical results that our algorithms perform well for sensitivity analysis. The ANOVA-boosting step reduces the approximation error of existing methods significantly.
|
[
"['Daniel Potts' 'Laura Weidensager']"
] |
null | null |
2404.03054
| null | null |
http://arxiv.org/pdf/2404.03054v2
|
2024-06-11T20:45:56Z
|
2024-04-03T20:38:22Z
|
Data-Driven Goal Recognition Design for General Behavioral Agents
|
Goal recognition design aims to make limited modifications to decision-making environments with the goal of making it easier to infer the goals of agents acting within those environments. Although various research efforts have been made in goal recognition design, existing approaches are computationally demanding and often assume that agents are (near-)optimal in their decision-making. To address these limitations, we introduce a data-driven approach to goal recognition design that can account for agents with general behavioral models. Following existing literature, we use worst-case distinctiveness($textit{wcd}$) as a measure of the difficulty in inferring the goal of an agent in a decision-making environment. Our approach begins by training a machine learning model to predict the $textit{wcd}$ for a given environment and the agent behavior model. We then propose a gradient-based optimization framework that accommodates various constraints to optimize decision-making environments for enhanced goal recognition. Through extensive simulations, we demonstrate that our approach outperforms existing methods in reducing $textit{wcd}$ and enhancing runtime efficiency in conventional setup. Moreover, our approach also adapts to settings in which existing approaches do not apply, such as those involving flexible budget constraints, more complex environments, and suboptimal agent behavior. Finally, we have conducted human-subject experiments which confirm that our method can create environments that facilitate efficient goal recognition from real-world human decision-makers.
|
[
"['Robert Kasumba' 'Guanghui Yu' 'Chien-Ju Ho' 'Sarah Keren' 'William Yeoh']"
] |
null | null |
2404.03058
| null | null |
http://arxiv.org/pdf/2404.03058v1
|
2024-04-03T20:50:48Z
|
2024-04-03T20:50:48Z
|
Automatic Extraction of Linguistic Description from Fuzzy Rule Base
|
Neuro-fuzzy systems are a technique of explainable artificial intelligence (XAI). They elaborate knowledge models as a set of fuzzy rules. Fuzzy sets are crucial components of fuzzy rules. They are used to model linguistic terms. In this paper, we present an automatic extraction of fuzzy rules in the natural English language. Full implementation is available free from a public repository.
|
[
"['Krzysztof Siminski' 'Konrad Wnuk']"
] |
null | null |
2404.03073
| null | null |
http://arxiv.org/pdf/2404.03073v1
|
2024-04-03T21:29:40Z
|
2024-04-03T21:29:40Z
|
Mai Ho'omāuna i ka 'Ai: Language Models Improve Automatic Speech
Recognition in Hawaiian
|
In this paper we address the challenge of improving Automatic Speech Recognition (ASR) for a low-resource language, Hawaiian, by incorporating large amounts of independent text data into an ASR foundation model, Whisper. To do this, we train an external language model (LM) on ~1.5M words of Hawaiian text. We then use the LM to rescore Whisper and compute word error rates (WERs) on a manually curated test set of labeled Hawaiian data. As a baseline, we use Whisper without an external LM. Experimental results reveal a small but significant improvement in WER when ASR outputs are rescored with a Hawaiian LM. The results support leveraging all available data in the development of ASR systems for underrepresented languages.
|
[
"['Kaavya Chaparala' 'Guido Zarrella' 'Bruce Torres Fischer' 'Larry Kimura'\n 'Oiwi Parker Jones']"
] |
null | null |
2404.03081
| null | null |
http://arxiv.org/pdf/2404.03081v1
|
2024-04-03T21:47:02Z
|
2024-04-03T21:47:02Z
|
First-order PDES for Graph Neural Networks: Advection And Burgers
Equation Models
|
Graph Neural Networks (GNNs) have established themselves as the preferred methodology in a multitude of domains, ranging from computer vision to computational biology, especially in contexts where data inherently conform to graph structures. While many existing methods have endeavored to model GNNs using various techniques, a prevalent challenge they grapple with is the issue of over-smoothing. This paper presents new Graph Neural Network models that incorporate two first-order Partial Differential Equations (PDEs). These models do not increase complexity but effectively mitigate the over-smoothing problem. Our experimental findings highlight the capacity of our new PDE model to achieve comparable results with higher-order PDE models and fix the over-smoothing problem up to 64 layers. These results underscore the adaptability and versatility of GNNs, indicating that unconventional approaches can yield outcomes on par with established techniques.
|
[
"['Yifan Qu' 'Oliver Krzysik' 'Hans De Sterck' 'Omer Ege Kara']"
] |
null | null |
2404.03082
| null | null |
http://arxiv.org/pdf/2404.03082v2
|
2024-05-26T19:16:11Z
|
2024-04-03T21:47:44Z
|
Machine Learning and Data Analysis Using Posets: A Survey
|
Posets are discrete mathematical structures which are ubiquitous in a broad range of data analysis and machine learning applications. Research connecting posets to the data science domain has been ongoing for many years. In this paper, a comprehensive review of a wide range of studies on data analysis and machine learning using posets are examined in terms of their theory, algorithms and applications. In addition, the applied lattice theory domain of formal concept analysis will also be highlighted in terms of its machine learning applications.
|
[
"['Arnauld Mesinga Mwafise']"
] |
null | null |
2404.03084
| null | null |
http://arxiv.org/pdf/2404.03084v1
|
2024-04-03T21:55:17Z
|
2024-04-03T21:55:17Z
|
Rethinking Teacher-Student Curriculum Learning through the Cooperative
Mechanics of Experience
|
Teacher-Student Curriculum Learning (TSCL) is a curriculum learning framework that draws inspiration from human cultural transmission and learning. It involves a teacher algorithm shaping the learning process of a learner algorithm by exposing it to controlled experiences. Despite its success, understanding the conditions under which TSCL is effective remains challenging. In this paper, we propose a data-centric perspective to analyze the underlying mechanics of the teacher-student interactions in TSCL. We leverage cooperative game theory to describe how the composition of the set of experiences presented by the teacher to the learner, as well as their order, influences the performance of the curriculum that is found by TSCL approaches. To do so, we demonstrate that for every TSCL problem, there exists an equivalent cooperative game, and several key components of the TSCL framework can be reinterpreted using game-theoretic principles. Through experiments covering supervised learning, reinforcement learning, and classical games, we estimate the cooperative values of experiences and use value-proportional curriculum mechanisms to construct curricula, even in cases where TSCL struggles. The framework and experimental setup we present in this work represent a novel foundation for a deeper exploration of TSCL, shedding light on its underlying mechanisms and providing insights into its broader applicability in machine learning.
|
[
"['Manfred Diaz' 'Liam Paull' 'Andrea Tacchetti']"
] |
null | null |
2404.03085
| null | null |
http://arxiv.org/abs/2404.03085v1
|
2024-04-03T21:55:44Z
|
2024-04-03T21:55:44Z
|
Talaria: Interactively Optimizing Machine Learning Models for Efficient
Inference
|
On-device machine learning (ML) moves computation from the cloud to personal devices, protecting user privacy and enabling intelligent user experiences. However, fitting models on devices with limited resources presents a major technical challenge: practitioners need to optimize models and balance hardware metrics such as model size, latency, and power. To help practitioners create efficient ML models, we designed and developed Talaria: a model visualization and optimization system. Talaria enables practitioners to compile models to hardware, interactively visualize model statistics, and simulate optimizations to test the impact on inference metrics. Since its internal deployment two years ago, we have evaluated Talaria using three methodologies: (1) a log analysis highlighting its growth of 800+ practitioners submitting 3,600+ models; (2) a usability survey with 26 users assessing the utility of 20 Talaria features; and (3) a qualitative interview with the 7 most active users about their experience using Talaria.
|
[
"['Fred Hohman' 'Chaoqun Wang' 'Jinmook Lee' 'Jochen Görtler'\n 'Dominik Moritz' 'Jeffrey P Bigham' 'Zhile Ren' 'Cecile Foret' 'Qi Shan'\n 'Xiaoyi Zhang']"
] |
null | null |
2404.03088
| null | null |
http://arxiv.org/pdf/2404.03088v1
|
2024-04-03T22:03:28Z
|
2024-04-03T22:03:28Z
|
Robust Federated Learning for Wireless Networks: A Demonstration with
Channel Estimation
|
Federated learning (FL) offers a privacy-preserving collaborative approach for training models in wireless networks, with channel estimation emerging as a promising application. Despite extensive studies on FL-empowered channel estimation, the security concerns associated with FL require meticulous attention. In a scenario where small base stations (SBSs) serve as local models trained on cached data, and a macro base station (MBS) functions as the global model setting, an attacker can exploit the vulnerability of FL, launching attacks with various adversarial attacks or deployment tactics. In this paper, we analyze such vulnerabilities, corresponding solutions were brought forth, and validated through simulation.
|
[
"['Zexin Fang' 'Bin Han' 'Hans D. Schotten']"
] |
null | null |
2404.03098
| null | null |
http://arxiv.org/pdf/2404.03098v1
|
2024-04-03T22:39:33Z
|
2024-04-03T22:39:33Z
|
Exploring the Trade-off Between Model Performance and Explanation
Plausibility of Text Classifiers Using Human Rationales
|
Saliency post-hoc explainability methods are important tools for understanding increasingly complex NLP models. While these methods can reflect the model's reasoning, they may not align with human intuition, making the explanations not plausible. In this work, we present a methodology for incorporating rationales, which are text annotations explaining human decisions, into text classification models. This incorporation enhances the plausibility of post-hoc explanations while preserving their faithfulness. Our approach is agnostic to model architectures and explainability methods. We introduce the rationales during model training by augmenting the standard cross-entropy loss with a novel loss function inspired by contrastive learning. By leveraging a multi-objective optimization algorithm, we explore the trade-off between the two loss functions and generate a Pareto-optimal frontier of models that balance performance and plausibility. Through extensive experiments involving diverse models, datasets, and explainability methods, we demonstrate that our approach significantly enhances the quality of model explanations without causing substantial (sometimes negligible) degradation in the original model's performance.
|
[
"['Lucas E. Resck' 'Marcos M. Raimundo' 'Jorge Poco']"
] |
null | null |
2404.03099
| null | null |
http://arxiv.org/pdf/2404.03099v1
|
2024-04-03T22:42:37Z
|
2024-04-03T22:42:37Z
|
Composite Bayesian Optimization In Function Spaces Using NEON -- Neural
Epistemic Operator Networks
|
Operator learning is a rising field of scientific computing where inputs or outputs of a machine learning model are functions defined in infinite-dimensional spaces. In this paper, we introduce NEON (Neural Epistemic Operator Networks), an architecture for generating predictions with uncertainty using a single operator network backbone, which presents orders of magnitude less trainable parameters than deep ensembles of comparable performance. We showcase the utility of this method for sequential decision-making by examining the problem of composite Bayesian Optimization (BO), where we aim to optimize a function $f=gcirc h$, where $h:Xto C(mathcal{Y},mathbb{R}^{d_s})$ is an unknown map which outputs elements of a function space, and $g: C(mathcal{Y},mathbb{R}^{d_s})to mathbb{R}$ is a known and cheap-to-compute functional. By comparing our approach to other state-of-the-art methods on toy and real world scenarios, we demonstrate that NEON achieves state-of-the-art performance while requiring orders of magnitude less trainable parameters.
|
[
"['Leonardo Ferreira Guilhoto' 'Paris Perdikaris']"
] |
null | null |
2404.03101
| null | null |
http://arxiv.org/pdf/2404.03101v1
|
2024-04-03T22:51:54Z
|
2024-04-03T22:51:54Z
|
MARL-LNS: Cooperative Multi-agent Reinforcement Learning via Large
Neighborhoods Search
|
Cooperative multi-agent reinforcement learning (MARL) has been an increasingly important research topic in the last half-decade because of its great potential for real-world applications. Because of the curse of dimensionality, the popular "centralized training decentralized execution" framework requires a long time in training, yet still cannot converge efficiently. In this paper, we propose a general training framework, MARL-LNS, to algorithmically address these issues by training on alternating subsets of agents using existing deep MARL algorithms as low-level trainers, while not involving any additional parameters to be trained. Based on this framework, we provide three algorithm variants based on the framework: random large neighborhood search (RLNS), batch large neighborhood search (BLNS), and adaptive large neighborhood search (ALNS), which alternate the subsets of agents differently. We test our algorithms on both the StarCraft Multi-Agent Challenge and Google Research Football, showing that our algorithms can automatically reduce at least 10% of training time while reaching the same final skill level as the original algorithm.
|
[
"['Weizhe Chen' 'Sven Koenig' 'Bistra Dilkina']"
] |
null | null |
2404.03105
| null | null |
http://arxiv.org/pdf/2404.03105v1
|
2024-04-03T23:07:24Z
|
2024-04-03T23:07:24Z
|
Methodology for Interpretable Reinforcement Learning for Optimizing
Mechanical Ventilation
|
Mechanical ventilation is a critical life-support intervention that uses a machine to deliver controlled air and oxygen to a patient's lungs, assisting or replacing spontaneous breathing. While several data-driven approaches have been proposed to optimize ventilator control strategies, they often lack interpretability and agreement with general domain knowledge. This paper proposes a methodology for interpretable reinforcement learning (RL) using decision trees for mechanical ventilation control. Using a causal, nonparametric model-based off-policy evaluation, we evaluate the policies in their ability to gain increases in SpO2 while avoiding aggressive ventilator settings which are known to cause ventilator induced lung injuries and other complications. Numerical experiments using MIMIC-III data on the stays of real patients' intensive care unit stays demonstrate that the decision tree policy outperforms the behavior cloning policy and is comparable to state-of-the-art RL policy. Future work concerns better aligning the cost function with medical objectives to generate deeper clinical insights.
|
[
"['Joo Seung Lee' 'Malini Mahendra' 'Anil Aswani']"
] |
null | null |
2404.03115
| null | null |
http://arxiv.org/pdf/2404.03115v1
|
2024-04-03T23:38:31Z
|
2024-04-03T23:38:31Z
|
Deep Learning-Based Weather-Related Power Outage Prediction with
Socio-Economic and Power Infrastructure Data
|
This paper presents a deep learning-based approach for hourly power outage probability prediction within census tracts encompassing a utility company's service territory. Two distinct deep learning models, conditional Multi-Layer Perceptron (MLP) and unconditional MLP, were developed to forecast power outage probabilities, leveraging a rich array of input features gathered from publicly available sources including weather data, weather station locations, power infrastructure maps, socio-economic and demographic statistics, and power outage records. Given a one-hour-ahead weather forecast, the models predict the power outage probability for each census tract, taking into account both the weather prediction and the location's characteristics. The deep learning models employed different loss functions to optimize prediction performance. Our experimental results underscore the significance of socio-economic factors in enhancing the accuracy of power outage predictions at the census tract level.
|
[
"['Xuesong Wang' 'Nina Fatehi' 'Caisheng Wang' 'Masoud H. Nazari']"
] |
null | null |
2404.03139
| null | null |
http://arxiv.org/pdf/2404.03139v1
|
2024-04-04T01:24:27Z
|
2024-04-04T01:24:27Z
|
Theoretical and Empirical Insights into the Origins of Degree Bias in
Graph Neural Networks
|
Graph Neural Networks (GNNs) often perform better for high-degree nodes than low-degree nodes on node classification tasks. This degree bias can reinforce social marginalization by, e.g., sidelining authors of lowly-cited papers when predicting paper topics in citation networks. While researchers have proposed numerous hypotheses for why GNN degree bias occurs, we find via a survey of 38 degree bias papers that these hypotheses are often not rigorously validated, and can even be contradictory. Thus, we provide an analysis of the origins of degree bias in message-passing GNNs with different graph filters. We prove that high-degree test nodes tend to have a lower probability of misclassification regardless of how GNNs are trained. Moreover, we show that degree bias arises from a variety of factors that are associated with a node's degree (e.g., homophily of neighbors, diversity of neighbors). Furthermore, we show that during training, some GNNs may adjust their loss on low-degree nodes more slowly than on high-degree nodes; however, with sufficiently many epochs of training, message-passing GNNs can achieve their maximum possible training accuracy, which is not significantly limited by their expressive power. Throughout our analysis, we connect our findings to previously-proposed hypotheses for the origins of degree bias, supporting and unifying some while drawing doubt to others. We validate our theoretical findings on 8 common real-world networks, and based on our theoretical and empirical insights, describe a roadmap to alleviate degree bias.
|
[
"['Arjun Subramonian' 'Jian Kang' 'Yizhou Sun']"
] |
null | null |
2404.03147
| null | null |
http://arxiv.org/pdf/2404.03147v5
|
2024-06-20T09:32:43Z
|
2024-04-04T01:42:28Z
|
Eigenpruning: an Interpretability-Inspired PEFT Method
|
We introduce eigenpruning, a method that removes singular values from weight matrices in an LLM to improve its performance in a particular task. This method is inspired by interpretability methods designed to automatically find subnetworks of a model which solve a specific task. In our tests, the pruned model outperforms the original model by a large margin, while only requiring minimal computation to prune the weight matrices. In the case of a small synthetic task in integer multiplication, the Phi-2 model can improve its accuracy in the test set from 13.75% to 97.50%. Interestingly, these results seem to indicate the existence of a computation path that can solve the task very effectively, but it was not being used by the original model. Finally, we publicly release our implementation.
|
[
"['Tomás Vergara-Browne' 'Álvaro Soto' 'Akiko Aizawa']"
] |
null | null |
2404.03163
| null | null |
http://arxiv.org/pdf/2404.03163v1
|
2024-04-04T02:31:05Z
|
2024-04-04T02:31:05Z
|
Uncertainty in Language Models: Assessment through Rank-Calibration
|
Language Models (LMs) have shown promising performance in natural language generation. However, as LMs often generate incorrect or hallucinated responses, it is crucial to correctly quantify their uncertainty in responding to given inputs. In addition to verbalized confidence elicited via prompting, many uncertainty measures ($e.g.$, semantic entropy and affinity-graph-based measures) have been proposed. However, these measures can differ greatly, and it is unclear how to compare them, partly because they take values over different ranges ($e.g.$, $[0,infty)$ or $[0,1]$). In this work, we address this issue by developing a novel and practical framework, termed $Rank$-$Calibration$, to assess uncertainty and confidence measures for LMs. Our key tenet is that higher uncertainty (or lower confidence) should imply lower generation quality, on average. Rank-calibration quantifies deviations from this ideal relationship in a principled manner, without requiring ad hoc binary thresholding of the correctness score ($e.g.$, ROUGE or METEOR). The broad applicability and the granular interpretability of our methods are demonstrated empirically.
|
[
"['Xinmeng Huang' 'Shuo Li' 'Mengxin Yu' 'Matteo Sesia' 'Hamed Hassani'\n 'Insup Lee' 'Osbert Bastani' 'Edgar Dobriban']"
] |
null | null |
2404.03164
| null | null |
http://arxiv.org/pdf/2404.03164v1
|
2024-04-04T02:32:58Z
|
2024-04-04T02:32:58Z
|
Does Knowledge Graph Really Matter for Recommender Systems?
|
Recommender systems (RSs) are designed to provide personalized recommendations to users. Recently, knowledge graphs (KGs) have been widely introduced in RSs to improve recommendation accuracy. In this study, however, we demonstrate that RSs do not necessarily perform worse even if the KG is downgraded to the user-item interaction graph only (or removed). We propose an evaluation framework KG4RecEval to systematically evaluate how much a KG contributes to the recommendation accuracy of a KG-based RS, using our defined metric KGER (KG utilization efficiency in recommendation). We consider the scenarios where knowledge in a KG gets completely removed, randomly distorted and decreased, and also where recommendations are for cold-start users. Our extensive experiments on four commonly used datasets and a number of state-of-the-art KG-based RSs reveal that: to remove, randomly distort or decrease knowledge does not necessarily decrease recommendation accuracy, even for cold-start users. These findings inspire us to rethink how to better utilize knowledge from existing KGs, whereby we discuss and provide insights into what characteristics of datasets and KG-based RSs may help improve KG utilization efficiency.
|
[
"['Haonan Zhang' 'Dongxia Wang' 'Zhu Sun' 'Yanhui Li' 'Youcheng Sun'\n 'Huizhi Liang' 'Wenhai Wang']"
] |
null | null |
2404.03171
| null | null |
http://arxiv.org/abs/2404.03171v1
|
2024-04-04T03:03:38Z
|
2024-04-04T03:03:38Z
|
Multi-modal Learning for WebAssembly Reverse Engineering
|
The increasing adoption of WebAssembly (Wasm) for performance-critical and security-sensitive tasks drives the demand for WebAssembly program comprehension and reverse engineering. Recent studies have introduced machine learning (ML)-based WebAssembly reverse engineering tools. Yet, the generalization of task-specific ML solutions remains challenging, because their effectiveness hinges on the availability of an ample supply of high-quality task-specific labeled data. Moreover, previous works overlook the high-level semantics present in source code and its documentation. Acknowledging the abundance of available source code with documentation, which can be compiled into WebAssembly, we propose to learn representations of them concurrently and harness their mutual relationships for effective WebAssembly reverse engineering. In this paper, we present WasmRev, the first multi-modal pre-trained language model for WebAssembly reverse engineering. WasmRev is pre-trained using self-supervised learning on a large-scale multi-modal corpus encompassing source code, code documentation and the compiled WebAssembly, without requiring labeled data. WasmRev incorporates three tailored multi-modal pre-training tasks to capture various characteristics of WebAssembly and cross-modal relationships. WasmRev is only trained once to produce general-purpose representations that can broadly support WebAssembly reverse engineering tasks through few-shot fine-tuning with much less labeled data, improving data efficiency. We fine-tune WasmRev onto three important reverse engineering tasks: type recovery, function purpose identification and WebAssembly summarization. Our results show that WasmRev pre-trained on the corpus of multi-modal samples establishes a robust foundation for these tasks, achieving high task accuracy and outperforming the state-of-the-art ML methods for WebAssembly reverse engineering.
|
[
"['Hanxian Huang' 'Jishen Zhao']"
] |
null | null |
2404.03176
| null | null |
http://arxiv.org/pdf/2404.03176v1
|
2024-04-04T03:20:35Z
|
2024-04-04T03:20:35Z
|
Information-Theoretic Generalization Bounds for Deep Neural Networks
|
Deep neural networks (DNNs) exhibit an exceptional capacity for generalization in practical applications. This work aims to capture the effect and benefits of depth for supervised learning via information-theoretic generalization bounds. We first derive two hierarchical bounds on the generalization error in terms of the Kullback-Leibler (KL) divergence or the 1-Wasserstein distance between the train and test distributions of the network internal representations. The KL divergence bound shrinks as the layer index increases, while the Wasserstein bound implies the existence of a layer that serves as a generalization funnel, which attains a minimal 1-Wasserstein distance. Analytic expressions for both bounds are derived under the setting of binary Gaussian classification with linear DNNs. To quantify the contraction of the relevant information measures when moving deeper into the network, we analyze the strong data processing inequality (SDPI) coefficient between consecutive layers of three regularized DNN models: Dropout, DropConnect, and Gaussian noise injection. This enables refining our generalization bounds to capture the contraction as a function of the network architecture parameters. Specializing our results to DNNs with a finite parameter space and the Gibbs algorithm reveals that deeper yet narrower network architectures generalize better in those examples, although how broadly this statement applies remains a question.
|
[
"['Haiyun He' 'Christina Lee Yu' 'Ziv Goldfeld']"
] |
null | null |
2404.03180
| null | null |
http://arxiv.org/pdf/2404.03180v2
|
2024-04-23T11:09:27Z
|
2024-04-04T03:29:41Z
|
Goldfish: An Efficient Federated Unlearning Framework
|
With recent legislation on the right to be forgotten, machine unlearning has emerged as a crucial research area. It facilitates the removal of a user's data from federated trained machine learning models without the necessity for retraining from scratch. However, current machine unlearning algorithms are confronted with challenges of efficiency and validity. To address the above issues, we propose a new framework, named Goldfish. It comprises four modules: basic model, loss function, optimization, and extension. To address the challenge of low validity in existing machine unlearning algorithms, we propose a novel loss function. It takes into account the loss arising from the discrepancy between predictions and actual labels in the remaining dataset. Simultaneously, it takes into consideration the bias of predicted results on the removed dataset. Moreover, it accounts for the confidence level of predicted results. Additionally, to enhance efficiency, we adopt knowledge a distillation technique in the basic model and introduce an optimization module that encompasses the early termination mechanism guided by empirical risk and the data partition mechanism. Furthermore, to bolster the robustness of the aggregated model, we propose an extension module that incorporates a mechanism using adaptive distillation temperature to address the heterogeneity of user local data and a mechanism using adaptive weight to handle the variety in the quality of uploaded models. Finally, we conduct comprehensive experiments to illustrate the effectiveness of proposed approach.
|
[
"['Houzhe Wang' 'Xiaojie Zhu' 'Chi Chen' 'Paulo Esteves-Veríssimo']"
] |
null | null |
2404.03188
| null | null |
http://arxiv.org/pdf/2404.03188v1
|
2024-04-04T04:16:31Z
|
2024-04-04T04:16:31Z
|
Classification of Nasopharyngeal Cases using DenseNet Deep Learning
Architecture
|
Nasopharyngeal carcinoma (NPC) is one of the understudied yet deadliest cancers in South East Asia. In Malaysia, the prevalence is identified mainly in Sarawak, among the ethnic of Bidayuh. NPC is often late-diagnosed because it is asymptomatic at the early stage. There are several tissue representations from the nasopharynx biopsy, such as nasopharyngeal inflammation (NPI), lymphoid hyperplasia (LHP), nasopharyngeal carcinoma (NPC) and normal tissue. This paper is our first initiative to identify the difference between NPC, NPI and normal cases. Seven whole slide images (WSIs) with gigapixel resolutions from seven different patients and two hospitals were experimented with using two test setups, consisting of a different set of images. The tissue regions are patched into smaller blocks and classified using DenseNet architecture with 21 dense layers. Two tests are carried out, each for proof of concept (Test 1) and real-test scenario (Test 2). The accuracy achieved for NPC class is 94.8% for Test 1 and 67.0% for Test 2.
|
[
"['W. S. H. M. W. Ahmad' 'M. F. A. Fauzi' 'M. K. Abdullahi'\n 'Jenny T. H. Lee' 'N. S. A. Basry' 'A Yahaya' 'A. M. Ismail' 'A. Adam'\n 'Elaine W. L. Chan' 'F. S. Abas']"
] |
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