categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2406.18747 | null | null | http://arxiv.org/pdf/2406.18747v1 | 2024-06-26T20:25:53Z | 2024-06-26T20:25:53Z | A Stem-Agnostic Single-Decoder System for Music Source Separation Beyond
Four Stems | Despite significant recent progress across multiple subtasks of audio source separation, few music source separation systems support separation beyond the four-stem vocals, drums, bass, and other (VDBO) setup. Of the very few current systems that support source separation beyond this setup, most continue to rely on an inflexible decoder setup that can only support a fixed pre-defined set of stems. Increasing stem support in these inflexible systems correspondingly requires increasing computational complexity, rendering extensions of these systems computationally infeasible for long-tail instruments. In this work, we propose Banquet, a system that allows source separation of multiple stems using just one decoder. A bandsplit source separation model is extended to work in a query-based setup in tandem with a music instrument recognition PaSST model. On the MoisesDB dataset, Banquet, at only 24.9 M trainable parameters, approached the performance level of the significantly more complex 6-stem Hybrid Transformer Demucs on VDBO stems and outperformed it on guitar and piano. The query-based setup allows for the separation of narrow instrument classes such as clean acoustic guitars, and can be successfully applied to the extraction of less common stems such as reeds and organs. Implementation is available at https://github.com/kwatcharasupat/query-bandit. | [
"['Karn N. Watcharasupat' 'Alexander Lerch']"
]
|
null | null | 2406.18752 | null | null | http://arxiv.org/pdf/2406.18752v1 | 2024-06-26T20:38:00Z | 2024-06-26T20:38:00Z | Competitive Algorithms for Online Knapsack with Succinct Predictions | In the online knapsack problem, the goal is to pack items arriving online with different values and weights into a capacity-limited knapsack to maximize the total value of the accepted items. We study textit{learning-augmented} algorithms for this problem, which aim to use machine-learned predictions to move beyond pessimistic worst-case guarantees. Existing learning-augmented algorithms for online knapsack consider relatively complicated prediction models that give an algorithm substantial information about the input, such as the total weight of items at each value. In practice, such predictions can be error-sensitive and difficult to learn. Motivated by this limitation, we introduce a family of learning-augmented algorithms for online knapsack that use emph{succinct predictions}. In particular, the machine-learned prediction given to the algorithm is just a single value or interval that estimates the minimum value of any item accepted by an offline optimal solution. By leveraging a relaxation to online emph{fractional} knapsack, we design algorithms that can leverage such succinct predictions in both the trusted setting (i.e., with perfect prediction) and the untrusted setting, where we prove that a simple meta-algorithm achieves a nearly optimal consistency-robustness trade-off. Empirically, we show that our algorithms significantly outperform baselines that do not use predictions and often outperform algorithms based on more complex prediction models. | [
"['Mohammadreza Daneshvaramoli' 'Helia Karisani' 'Adam Lechowicz' 'Bo Sun'\n 'Cameron Musco' 'Mohammad Hajiesmaili']"
]
|
null | null | 2406.18757 | null | null | http://arxiv.org/pdf/2406.18757v2 | 2024-06-28T17:12:37Z | 2024-06-26T20:55:26Z | The Impact of Feature Representation on the Accuracy of Photonic Neural
Networks | Photonic Neural Networks (PNNs) are gaining significant interest in the research community due to their potential for high parallelization, low latency, and energy efficiency. PNNs compute using light, which leads to several differences in implementation when compared to electronics, such as the need to represent input features in the photonic domain before feeding them into the network. In this encoding process, it is common to combine multiple features into a single input to reduce the number of inputs and associated devices, leading to smaller and more energy-efficient PNNs. Although this alters the network's handling of input data, its impact on PNNs remains understudied. This paper addresses this open question, investigating the effect of commonly used encoding strategies that combine features on the performance and learning capabilities of PNNs. Here, using the concept of feature importance, we develop a mathematical methodology for analyzing feature combination. Through this methodology, we demonstrate that encoding multiple features together in a single input determines their relative importance, thus limiting the network's ability to learn from the data. Given some prior knowledge of the data, however, this can also be leveraged for higher accuracy. By selecting an optimal encoding method, we achieve up to a 12.3% improvement in accuracy of PNNs trained on the Iris dataset compared to other encoding techniques, surpassing the performance of networks where features are not combined. These findings highlight the importance of carefully choosing the encoding to the accuracy and decision-making strategies of PNNs, particularly in size or power constrained applications. | [
"['Mauricio Gomes de Queiroz' 'Paul Jimenez' 'Raphael Cardoso'\n 'Mateus Vidaletti Costa' 'Mohab Abdalla' \"Ian O'Connor\" 'Alberto Bosio'\n 'Fabio Pavanello']"
]
|
null | null | 2406.18763 | null | null | http://arxiv.org/pdf/2406.18763v1 | 2024-06-26T21:17:37Z | 2024-06-26T21:17:37Z | Conformalized Link Prediction on Graph Neural Networks | Graph Neural Networks (GNNs) excel in diverse tasks, yet their applications in high-stakes domains are often hampered by unreliable predictions. Although numerous uncertainty quantification methods have been proposed to address this limitation, they often lack textit{rigorous} uncertainty estimates. This work makes the first attempt to introduce a distribution-free and model-agnostic uncertainty quantification approach to construct a predictive interval with a statistical guarantee for GNN-based link prediction. We term it as textit{conformalized link prediction.} Our approach builds upon conformal prediction (CP), a framework that promises to construct statistically robust prediction sets or intervals. We first theoretically and empirically establish a permutation invariance condition for the application of CP in link prediction tasks, along with an exact test-time coverage. Leveraging the important structural information in graphs, we then identify a novel and crucial connection between a graph's adherence to the power law distribution and the efficiency of CP. This insight leads to the development of a simple yet effective sampling-based method to align the graph structure with a power law distribution prior to the standard CP procedure. Extensive experiments demonstrate that for conformalized link prediction, our approach achieves the desired marginal coverage while significantly improving the efficiency of CP compared to baseline methods. | [
"['Tianyi Zhao' 'Jian Kang' 'Lu Cheng']"
]
|
null | null | 2406.18765 | null | null | http://arxiv.org/pdf/2406.18765v1 | 2024-06-26T21:30:41Z | 2024-06-26T21:30:41Z | WV-Net: A foundation model for SAR WV-mode satellite imagery trained
using contrastive self-supervised learning on 10 million images | The European Space Agency's Copernicus Sentinel-1 (S-1) mission is a constellation of C-band synthetic aperture radar (SAR) satellites that provide unprecedented monitoring of the world's oceans. S-1's wave mode (WV) captures 20x20 km image patches at 5 m pixel resolution and is unaffected by cloud cover or time-of-day. The mission's open data policy has made SAR data easily accessible for a range of applications, but the need for manual image annotations is a bottleneck that hinders the use of machine learning methods. This study uses nearly 10 million WV-mode images and contrastive self-supervised learning to train a semantic embedding model called WV-Net. In multiple downstream tasks, WV-Net outperforms a comparable model that was pre-trained on natural images (ImageNet) with supervised learning. Experiments show improvements for estimating wave height (0.50 vs 0.60 RMSE using linear probing), estimating near-surface air temperature (0.90 vs 0.97 RMSE), and performing multilabel-classification of geophysical and atmospheric phenomena (0.96 vs 0.95 micro-averaged AUROC). WV-Net embeddings are also superior in an unsupervised image-retrieval task and scale better in data-sparse settings. Together, these results demonstrate that WV-Net embeddings can support geophysical research by providing a convenient foundation model for a variety of data analysis and exploration tasks. | [
"['Yannik Glaser' 'Justin E. Stopa' 'Linnea M. Wolniewicz' 'Ralph Foster'\n 'Doug Vandemark' 'Alexis Mouche' 'Bertrand Chapron' 'Peter Sadowski']"
]
|
null | null | 2406.18770 | null | null | http://arxiv.org/pdf/2406.18770v1 | 2024-06-26T21:42:50Z | 2024-06-26T21:42:50Z | ADO-LLM: Analog Design Bayesian Optimization with In-Context Learning of
Large Language Models | Analog circuit design requires substantial human expertise and involvement, which is a significant roadblock to design productivity. Bayesian Optimization (BO), a popular machine learning based optimization strategy, has been leveraged to automate analog design given its applicability across various circuit topologies and technologies. Traditional BO methods employ black box Gaussian Process surrogate models and optimized labeled data queries to find optimization solutions by trading off between exploration and exploitation. However, the search for the optimal design solution in BO can be expensive from both a computational and data usage point of view, particularly for high dimensional optimization problems. This paper presents ADO-LLM, the first work integrating large language models (LLMs) with Bayesian Optimization for analog design optimization. ADO-LLM leverages the LLM's ability to infuse domain knowledge to rapidly generate viable design points to remedy BO's inefficiency in finding high value design areas specifically under the limited design space coverage of the BO's probabilistic surrogate model. In the meantime, sampling of design points evaluated in the iterative BO process provides quality demonstrations for the LLM to generate high quality design points while leveraging infused broad design knowledge. Furthermore, the diversity brought by BO's exploration enriches the contextual understanding of the LLM and allows it to more broadly search in the design space and prevent repetitive and redundant suggestions. We evaluate the proposed framework on two different types of analog circuits and demonstrate notable improvements in design efficiency and effectiveness. | [
"['Yuxuan Yin' 'Yu Wang' 'Boxun Xu' 'Peng Li']"
]
|
null | null | 2406.18777 | null | null | http://arxiv.org/pdf/2406.18777v1 | 2024-06-26T22:24:46Z | 2024-06-26T22:24:46Z | Aligning Model Properties via Conformal Risk Control | AI model alignment is crucial due to inadvertent biases in training data and the underspecified pipeline in modern machine learning, where numerous models with excellent test set metrics can be produced, yet they may not meet end-user requirements. Recent advances demonstrate that post-training model alignment via human feedback can address some of these challenges. However, these methods are often confined to settings (such as generative AI) where humans can interpret model outputs and provide feedback. In traditional non-generative settings, where model outputs are numerical values or classes, detecting misalignment through single-sample outputs is highly challenging. In this paper we consider an alternative strategy. We propose interpreting model alignment through property testing, defining an aligned model $f$ as one belonging to a subset $mathcal{P}$ of functions that exhibit specific desired behaviors. We focus on post-processing a pre-trained model $f$ to better align with $mathcal{P}$ using conformal risk control. Specifically, we develop a general procedure for converting queries for a given property $mathcal{P}$ to a collection of loss functions suitable for use in a conformal risk control algorithm. We prove a probabilistic guarantee that the resulting conformal interval around $f$ contains a function approximately satisfying $mathcal{P}$. Given the capabilities of modern AI models with extensive parameters and training data, one might assume alignment issues will resolve naturally. However, increasing training data or parameters in a random feature model doesn't eliminate the need for alignment techniques when pre-training data is biased. We demonstrate our alignment methodology on supervised learning datasets for properties like monotonicity and concavity. Our flexible procedure can be applied to various desired properties. | [
"['William Overman' 'Jacqueline Jil Vallon' 'Mohsen Bayati']"
]
|
null | null | 2406.18781 | null | null | http://arxiv.org/pdf/2406.18781v1 | 2024-06-26T22:50:43Z | 2024-06-26T22:50:43Z | Learning to Remove Cuts in Integer Linear Programming | Cutting plane methods are a fundamental approach for solving integer linear programs (ILPs). In each iteration of such methods, additional linear constraints (cuts) are introduced to the constraint set with the aim of excluding the previous fractional optimal solution while not affecting the optimal integer solution. In this work, we explore a novel approach within cutting plane methods: instead of only adding new cuts, we also consider the removal of previous cuts introduced at any of the preceding iterations of the method under a learnable parametric criteria. We demonstrate that in fundamental combinatorial optimization settings such cut removal policies can lead to significant improvements over both human-based and machine learning-guided cut addition policies even when implemented with simple models. | [
"['Pol Puigdemont' 'Stratis Skoulakis' 'Grigorios Chrysos' 'Volkan Cevher']"
]
|
null | null | 2406.18783 | null | null | http://arxiv.org/pdf/2406.18783v2 | 2024-06-28T21:22:56Z | 2024-06-26T23:04:52Z | Psychological Profiling in Cybersecurity: A Look at LLMs and
Psycholinguistic Features | The increasing sophistication of cyber threats necessitates innovative approaches to cybersecurity. In this paper, we explore the potential of psychological profiling techniques, particularly focusing on the utilization of Large Language Models (LLMs) and psycholinguistic features. We investigate the intersection of psychology and cybersecurity, discussing how LLMs can be employed to analyze textual data for identifying psychological traits of threat actors. We explore the incorporation of psycholinguistic features, such as linguistic patterns and emotional cues, into cybersecurity frameworks. Our research underscores the importance of integrating psychological perspectives into cybersecurity practices to bolster defense mechanisms against evolving threats. | [
"['Jean Marie Tshimula' \"D'Jeff K. Nkashama\" 'Jean Tshibangu Muabila'\n 'René Manassé Galekwa' 'Hugues Kanda' 'Maximilien V. Dialufuma'\n 'Mbuyi Mukendi Didier' 'Kalala Kalonji' 'Serge Mundele'\n 'Patience Kinshie Lenye' 'Tighana Wenge Basele' 'Aristarque Ilunga'\n 'Christian N. Mayemba' 'Nathanaël M. Kasoro' 'Selain K. Kasereka'\n 'Hardy Mikese' 'Pierre-Martin Tardif' 'Marc Frappier' 'Froduald Kabanza'\n 'Belkacem Chikhaoui' 'Shengrui Wang' 'Ali Mulenda Sumbu' 'Xavier Ndona'\n 'Raoul Kienge-Kienge Intudi']"
]
|
null | null | 2406.18787 | null | null | http://arxiv.org/pdf/2406.18787v1 | 2024-06-26T23:13:45Z | 2024-06-26T23:13:45Z | Unified Uncertainties: Combining Input, Data and Model Uncertainty into
a Single Formulation | Modelling uncertainty in Machine Learning models is essential for achieving safe and reliable predictions. Most research on uncertainty focuses on output uncertainty (predictions), but minimal attention is paid to uncertainty at inputs. We propose a method for propagating uncertainty in the inputs through a Neural Network that is simultaneously able to estimate input, data, and model uncertainty. Our results show that this propagation of input uncertainty results in a more stable decision boundary even under large amounts of input noise than comparatively simple Monte Carlo sampling. Additionally, we discuss and demonstrate that input uncertainty, when propagated through the model, results in model uncertainty at the outputs. The explicit incorporation of input uncertainty may be beneficial in situations where the amount of input uncertainty is known, though good datasets for this are still needed. | [
"['Matias Valdenegro-Toro' 'Ivo Pascal de Jong' 'Marco Zullich']"
]
|
null | null | 2406.18794 | null | null | http://arxiv.org/pdf/2406.18794v2 | 2024-07-02T18:13:03Z | 2024-06-26T23:36:46Z | Operator Learning of Lipschitz Operators: An Information-Theoretic
Perspective | Operator learning based on neural operators has emerged as a promising paradigm for the data-driven approximation of operators, mapping between infinite-dimensional Banach spaces. Despite significant empirical progress, our theoretical understanding regarding the efficiency of these approximations remains incomplete. This work addresses the parametric complexity of neural operator approximations for the general class of Lipschitz continuous operators. Motivated by recent findings on the limitations of specific architectures, termed curse of parametric complexity, we here adopt an information-theoretic perspective. Our main contribution establishes lower bounds on the metric entropy of Lipschitz operators in two approximation settings; uniform approximation over a compact set of input functions, and approximation in expectation, with input functions drawn from a probability measure. It is shown that these entropy bounds imply that, regardless of the activation function used, neural operator architectures attaining an approximation accuracy $epsilon$ must have a size that is exponentially large in $epsilon^{-1}$. The size of architectures is here measured by counting the number of encoded bits necessary to store the given model in computational memory. The results of this work elucidate fundamental trade-offs and limitations in operator learning. | [
"['Samuel Lanthaler']"
]
|
null | null | 2406.18800 | null | null | http://arxiv.org/pdf/2406.18800v1 | 2024-06-27T00:15:54Z | 2024-06-27T00:15:54Z | Infinite Width Models That Work: Why Feature Learning Doesn't Matter as
Much as You Think | Common infinite-width architectures such as Neural Tangent Kernels (NTKs) have historically shown weak performance compared to finite models. This has been attributed to the absence of feature learning. We show that this is not the case. In fact, we show that infinite width NTK models are able to access richer features than finite models by selecting relevant subfeatures from their (infinite) feature vector. In fact, we show experimentally that NTKs under-perform traditional finite models even when feature learning is artificially disabled. Instead, weak performance is due to the fact that existing constructions depend on weak optimizers like SGD. We provide an infinite width limit based on ADAM-like learning dynamics and demonstrate empirically that the resulting models erase this performance gap. | [
"['Luke Sernau']"
]
|
null | null | 2406.18802 | null | null | http://arxiv.org/pdf/2406.18802v1 | 2024-06-27T00:21:10Z | 2024-06-27T00:21:10Z | All Random Features Representations are Equivalent | Random features are an important technique that make it possible to rewrite positive-definite kernels as infinite-dimensional dot products. Over time, increasingly elaborate random feature representations have been developed in pursuit of finite approximations with ever lower error. We resolve this arms race by deriving an optimal sampling policy, and show that under this policy all random features representations have the same approximation error. This establishes a lower bound that holds across all random feature representations, and shows that we are free to choose whatever representation we please, provided we sample optimally. | [
"['Luke Sernau' 'Silvano Bonacina' 'Rif A. Saurous']"
]
|
null | null | 2406.18805 | null | null | http://arxiv.org/pdf/2406.18805v1 | 2024-06-27T00:42:33Z | 2024-06-27T00:42:33Z | Online Stackelberg Optimization via Nonlinear Control | In repeated interaction problems with adaptive agents, our objective often requires anticipating and optimizing over the space of possible agent responses. We show that many problems of this form can be cast as instances of online (nonlinear) control which satisfy textit{local controllability}, with convex losses over a bounded state space which encodes agent behavior, and we introduce a unified algorithmic framework for tractable regret minimization in such cases. When the instance dynamics are known but otherwise arbitrary, we obtain oracle-efficient $O(sqrt{T})$ regret by reduction to online convex optimization, which can be made computationally efficient if dynamics are locally textit{action-linear}. In the presence of adversarial disturbances to the state, we give tight bounds in terms of either the cumulative or per-round disturbance magnitude (for textit{strongly} or textit{weakly} locally controllable dynamics, respectively). Additionally, we give sublinear regret results for the cases of unknown locally action-linear dynamics as well as for the bandit feedback setting. Finally, we demonstrate applications of our framework to well-studied problems including performative prediction, recommendations for adaptive agents, adaptive pricing of real-valued goods, and repeated gameplay against no-regret learners, directly yielding extensions beyond prior results in each case. | [
"['William Brown' 'Christos Papadimitriou' 'Tim Roughgarden']"
]
|
null | null | 2406.18806 | null | null | http://arxiv.org/pdf/2406.18806v1 | 2024-06-27T00:44:46Z | 2024-06-27T00:44:46Z | Density Ratio Estimation via Sampling along Generalized Geodesics on
Statistical Manifolds | The density ratio of two probability distributions is one of the fundamental tools in mathematical and computational statistics and machine learning, and it has a variety of known applications. Therefore, density ratio estimation from finite samples is a very important task, but it is known to be unstable when the distributions are distant from each other. One approach to address this problem is density ratio estimation using incremental mixtures of the two distributions. We geometrically reinterpret existing methods for density ratio estimation based on incremental mixtures. We show that these methods can be regarded as iterating on the Riemannian manifold along a particular curve between the two probability distributions. Making use of the geometry of the manifold, we propose to consider incremental density ratio estimation along generalized geodesics on this manifold. To achieve such a method requires Monte Carlo sampling along geodesics via transformations of the two distributions. We show how to implement an iterative algorithm to sample along these geodesics and show how changing the distances along the geodesic affect the variance and accuracy of the estimation of the density ratio. Our experiments demonstrate that the proposed approach outperforms the existing approaches using incremental mixtures that do not take the geometry of the | [
"['Masanari Kimura' 'Howard Bondell']"
]
|
null | null | 2406.18814 | null | null | http://arxiv.org/pdf/2406.18814v1 | 2024-06-27T01:08:04Z | 2024-06-27T01:08:04Z | Length Optimization in Conformal Prediction | Conditional validity and length efficiency are two crucial aspects of conformal prediction (CP). Achieving conditional validity ensures accurate uncertainty quantification for data subpopulations, while proper length efficiency ensures that the prediction sets remain informative and non-trivial. Despite significant efforts to address each of these issues individually, a principled framework that reconciles these two objectives has been missing in the CP literature. In this paper, we develop Conformal Prediction with Length-Optimization (CPL) - a novel framework that constructs prediction sets with (near-) optimal length while ensuring conditional validity under various classes of covariate shifts, including the key cases of marginal and group-conditional coverage. In the infinite sample regime, we provide strong duality results which indicate that CPL achieves conditional validity and length optimality. In the finite sample regime, we show that CPL constructs conditionally valid prediction sets. Our extensive empirical evaluations demonstrate the superior prediction set size performance of CPL compared to state-of-the-art methods across diverse real-world and synthetic datasets in classification, regression, and text-related settings. | [
"['Shayan Kiyani' 'George Pappas' 'Hamed Hassani']"
]
|
null | null | 2406.18815 | null | null | http://arxiv.org/pdf/2406.18815v1 | 2024-06-27T01:09:07Z | 2024-06-27T01:09:07Z | MissionGNN: Hierarchical Multimodal GNN-based Weakly Supervised Video
Anomaly Recognition with Mission-Specific Knowledge Graph Generation | In the context of escalating safety concerns across various domains, the tasks of Video Anomaly Detection (VAD) and Video Anomaly Recognition (VAR) have emerged as critically important for applications in intelligent surveillance, evidence investigation, violence alerting, etc. These tasks, aimed at identifying and classifying deviations from normal behavior in video data, face significant challenges due to the rarity of anomalies which leads to extremely imbalanced data and the impracticality of extensive frame-level data annotation for supervised learning. This paper introduces a novel hierarchical graph neural network (GNN) based model MissionGNN that addresses these challenges by leveraging a state-of-the-art large language model and a comprehensive knowledge graph for efficient weakly supervised learning in VAR. Our approach circumvents the limitations of previous methods by avoiding heavy gradient computations on large multimodal models and enabling fully frame-level training without fixed video segmentation. Utilizing automated, mission-specific knowledge graph generation, our model provides a practical and efficient solution for real-time video analysis without the constraints of previous segmentation-based or multimodal approaches. Experimental validation on benchmark datasets demonstrates our model's performance in VAD and VAR, highlighting its potential to redefine the landscape of anomaly detection and recognition in video surveillance systems. | [
"['Sanggeon Yun' 'Ryozo Masukawa' 'Minhyoung Na' 'Mohsen Imani']"
]
|
null | null | 2406.18820 | null | null | http://arxiv.org/pdf/2406.18820v2 | 2024-06-28T02:33:11Z | 2024-06-27T01:28:30Z | Universal Checkpointing: Efficient and Flexible Checkpointing for Large
Scale Distributed Training | Existing checkpointing approaches seem ill-suited for distributed training even though hardware limitations make model parallelism, i.e., sharding model state across multiple accelerators, a requirement for model scaling. Consolidating distributed model state into a single checkpoint unacceptably slows down training, and is impractical at extreme scales. Distributed checkpoints, in contrast, are tightly coupled to the model parallelism and hardware configurations of the training run, and thus unusable on different configurations. To address this problem, we propose Universal Checkpointing, a technique that enables efficient checkpoint creation while providing the flexibility of resuming on arbitrary parallelism strategy and hardware configurations. Universal Checkpointing unlocks unprecedented capabilities for large-scale training such as improved resilience to hardware failures through continued training on remaining healthy hardware, and reduced training time through opportunistic exploitation of elastic capacity. The key insight of Universal Checkpointing is the selection of the optimal representation in each phase of the checkpointing life cycle: distributed representation for saving, and consolidated representation for loading. This is achieved using two key mechanisms. First, the universal checkpoint format, which consists of a consolidated representation of each model parameter and metadata for mapping parameter fragments into training ranks of arbitrary model-parallelism configuration. Second, the universal checkpoint language, a simple but powerful specification language for converting distributed checkpoints into the universal checkpoint format. Our evaluation demonstrates the effectiveness and generality of Universal Checkpointing on state-of-the-art model architectures and a wide range of parallelism techniques. | [
"['Xinyu Lian' 'Sam Ade Jacobs' 'Lev Kurilenko' 'Masahiro Tanaka'\n 'Stas Bekman' 'Olatunji Ruwase' 'Minjia Zhang']"
]
|
null | null | 2406.18847 | null | null | http://arxiv.org/abs/2406.18847v1 | 2024-06-27T02:38:13Z | 2024-06-27T02:38:13Z | Learning Retrieval Augmentation for Personalized Dialogue Generation | Personalized dialogue generation, focusing on generating highly tailored responses by leveraging persona profiles and dialogue context, has gained significant attention in conversational AI applications. However, persona profiles, a prevalent setting in current personalized dialogue datasets, typically composed of merely four to five sentences, may not offer comprehensive descriptions of the persona about the agent, posing a challenge to generate truly personalized dialogues. To handle this problem, we propose $textbf{L}$earning Retrieval $textbf{A}$ugmentation for $textbf{P}$ersonalized $textbf{D}$ial$textbf{O}$gue $textbf{G}$eneration ($textbf{LAPDOG}$), which studies the potential of leveraging external knowledge for persona dialogue generation. Specifically, the proposed LAPDOG model consists of a story retriever and a dialogue generator. The story retriever uses a given persona profile as queries to retrieve relevant information from the story document, which serves as a supplementary context to augment the persona profile. The dialogue generator utilizes both the dialogue history and the augmented persona profile to generate personalized responses. For optimization, we adopt a joint training framework that collaboratively learns the story retriever and dialogue generator, where the story retriever is optimized towards desired ultimate metrics (e.g., BLEU) to retrieve content for the dialogue generator to generate personalized responses. Experiments conducted on the CONVAI2 dataset with ROCStory as a supplementary data source show that the proposed LAPDOG method substantially outperforms the baselines, indicating the effectiveness of the proposed method. The LAPDOG model code is publicly available for further exploration. https://github.com/hqsiswiliam/LAPDOG | [
"['Qiushi Huang' 'Shuai Fu' 'Xubo Liu' 'Wenwu Wang' 'Tom Ko' 'Yu Zhang'\n 'Lilian Tang']"
]
|
null | null | 2406.18848 | null | null | http://arxiv.org/pdf/2406.18848v1 | 2024-06-27T02:38:25Z | 2024-06-27T02:38:25Z | Temporally Multi-Scale Sparse Self-Attention for Physical Activity Data
Imputation | Wearable sensors enable health researchers to continuously collect data pertaining to the physiological state of individuals in real-world settings. However, such data can be subject to extensive missingness due to a complex combination of factors. In this work, we study the problem of imputation of missing step count data, one of the most ubiquitous forms of wearable sensor data. We construct a novel and large scale data set consisting of a training set with over 3 million hourly step count observations and a test set with over 2.5 million hourly step count observations. We propose a domain knowledge-informed sparse self-attention model for this task that captures the temporal multi-scale nature of step-count data. We assess the performance of the model relative to baselines and conduct ablation studies to verify our specific model designs. | [
"['Hui Wei' 'Maxwell A. Xu' 'Colin Samplawski' 'James M. Rehg'\n 'Santosh Kumar' 'Benjamin M. Marlin']"
]
|
null | null | 2406.18851 | null | null | http://arxiv.org/pdf/2406.18851v1 | 2024-06-27T02:43:18Z | 2024-06-27T02:43:18Z | LICO: Large Language Models for In-Context Molecular Optimization | Optimizing black-box functions is a fundamental problem in science and engineering. To solve this problem, many approaches learn a surrogate function that estimates the underlying objective from limited historical evaluations. Large Language Models (LLMs), with their strong pattern-matching capabilities via pretraining on vast amounts of data, stand out as a potential candidate for surrogate modeling. However, directly prompting a pretrained language model to produce predictions is not feasible in many scientific domains due to the scarcity of domain-specific data in the pretraining corpora and the challenges of articulating complex problems in natural language. In this work, we introduce LICO, a general-purpose model that extends arbitrary base LLMs for black-box optimization, with a particular application to the molecular domain. To achieve this, we equip the language model with a separate embedding layer and prediction layer, and train the model to perform in-context predictions on a diverse set of functions defined over the domain. Once trained, LICO can generalize to unseen molecule properties simply via in-context prompting. LICO achieves state-of-the-art performance on PMO, a challenging molecular optimization benchmark comprising over 20 objective functions. | [
"['Tung Nguyen' 'Aditya Grover']"
]
|
null | null | 2406.18853 | null | null | http://arxiv.org/pdf/2406.18853v2 | 2024-06-29T02:29:38Z | 2024-06-27T02:46:30Z | Decoding-Time Language Model Alignment with Multiple Objectives | Aligning language models (LMs) to human preferences has emerged as a critical pursuit, enabling these models to better serve diverse user needs. Existing methods primarily focus on optimizing LMs for a single reward function, limiting their adaptability to varied objectives. Here, we propose $textbf{multi-objective decoding (MOD)}$, a decoding-time algorithm that outputs the next token from a linear combination of predictions of all base models, for any given weightings over different objectives. We exploit a common form among a family of $f$-divergence regularized alignment approaches (such as PPO, DPO, and their variants) to identify a closed-form solution by Legendre transform, and derive an efficient decoding strategy. Theoretically, we show why existing approaches can be sub-optimal even in natural settings and obtain optimality guarantees for our method. Empirical results demonstrate the effectiveness of the algorithm. For example, compared to a parameter-merging baseline, MOD achieves 12.8% overall reward improvement when equally optimizing towards $3$ objectives. Moreover, we experiment with MOD on combining three fully-finetuned LLMs of different model sizes, each aimed at different objectives such as safety, coding, and general user preference. Unlike traditional methods that require careful curation of a mixture of datasets to achieve comprehensive improvement, we can quickly experiment with preference weightings using MOD to find the best combination of models. Our best combination reduces toxicity on Toxigen to nearly 0% and achieves 7.9--33.3% improvement across other three metrics ($textit{i.e.}$, Codex@1, GSM-COT, BBH-COT). | [
"['Ruizhe Shi' 'Yifang Chen' 'Yushi Hu' 'Alisa Liu' 'Hannaneh Hajishirzi'\n 'Noah A. Smith' 'Simon Du']"
]
|
null | null | 2406.18854 | null | null | http://arxiv.org/pdf/2406.18854v1 | 2024-06-27T02:48:33Z | 2024-06-27T02:48:33Z | What Is Missing In Homophily? Disentangling Graph Homophily For Graph
Neural Networks | Graph homophily refers to the phenomenon that connected nodes tend to share similar characteristics. Understanding this concept and its related metrics is crucial for designing effective Graph Neural Networks (GNNs). The most widely used homophily metrics, such as edge or node homophily, quantify such "similarity" as label consistency across the graph topology. These metrics are believed to be able to reflect the performance of GNNs, especially on node-level tasks. However, many recent studies have empirically demonstrated that the performance of GNNs does not always align with homophily metrics, and how homophily influences GNNs still remains unclear and controversial. Then, a crucial question arises: What is missing in our current understanding of homophily? To figure out the missing part, in this paper, we disentangle the graph homophily into $3$ aspects: label, structural, and feature homophily, providing a more comprehensive understanding of GNN performance. To investigate their synergy, we propose a Contextual Stochastic Block Model with $3$ types of Homophily (CSBM-3H), where the topology and feature generation are controlled by the $3$ metrics. Based on the theoretical analysis of CSBM-3H, we derive a new composite metric, named Tri-Hom, that considers all $3$ aspects and overcomes the limitations of conventional homophily metrics. The theoretical conclusions and the effectiveness of Tri-Hom have been verified through synthetic experiments on CSBM-3H. In addition, we conduct experiments on $31$ real-world benchmark datasets and calculate the correlations between homophily metrics and model performance. Tri-Hom has significantly higher correlation values than $17$ existing metrics that only focus on a single homophily aspect, demonstrating its superiority and the importance of homophily synergy. Our code is available at url{https://github.com/zylMozart/Disentangle_GraphHom}. | [
"['Yilun Zheng' 'Sitao Luan' 'Lihui Chen']"
]
|
null | null | 2406.18861 | null | null | http://arxiv.org/pdf/2406.18861v2 | 2024-07-05T03:03:45Z | 2024-06-27T03:16:09Z | Predicting the duration of traffic incidents for Sydney greater
metropolitan area using machine learning methods | This research presents a comprehensive approach to predicting the duration of traffic incidents and classifying them as short-term or long-term across the Sydney Metropolitan Area. Leveraging a dataset that encompasses detailed records of traffic incidents, road network characteristics, and socio-economic indicators, we train and evaluate a variety of advanced machine learning models including Gradient Boosted Decision Trees (GBDT), Random Forest, LightGBM, and XGBoost. The models are assessed using Root Mean Square Error (RMSE) for regression tasks and F1 score for classification tasks. Our experimental results demonstrate that XGBoost and LightGBM outperform conventional models with XGBoost achieving the lowest RMSE of 33.7 for predicting incident duration and highest classification F1 score of 0.62 for a 30-minute duration threshold. For classification, the 30-minute threshold balances performance with 70.84% short-term duration classification accuracy and 62.72% long-term duration classification accuracy. Feature importance analysis, employing both tree split counts and SHAP values, identifies the number of affected lanes, traffic volume, and types of primary and secondary vehicles as the most influential features. The proposed methodology not only achieves high predictive accuracy but also provides stakeholders with vital insights into factors contributing to incident durations. These insights enable more informed decision-making for traffic management and response strategies. The code is available by the link: https://github.com/Future-Mobility-Lab/SydneyIncidents | [
"['Artur Grigorev' 'Sajjad Shafiei' 'Hanna Grzybowska'\n 'Adriana-Simona Mihaita']"
]
|
null | null | 2406.18865 | null | null | http://arxiv.org/pdf/2406.18865v1 | 2024-06-27T03:33:38Z | 2024-06-27T03:33:38Z | From Biased Selective Labels to Pseudo-Labels: An
Expectation-Maximization Framework for Learning from Biased Decisions | Selective labels occur when label observations are subject to a decision-making process; e.g., diagnoses that depend on the administration of laboratory tests. We study a clinically-inspired selective label problem called disparate censorship, where labeling biases vary across subgroups and unlabeled individuals are imputed as "negative" (i.e., no diagnostic test = no illness). Machine learning models naively trained on such labels could amplify labeling bias. Inspired by causal models of selective labels, we propose Disparate Censorship Expectation-Maximization (DCEM), an algorithm for learning in the presence of disparate censorship. We theoretically analyze how DCEM mitigates the effects of disparate censorship on model performance. We validate DCEM on synthetic data, showing that it improves bias mitigation (area between ROC curves) without sacrificing discriminative performance (AUC) compared to baselines. We achieve similar results in a sepsis classification task using clinical data. | [
"['Trenton Chang' 'Jenna Wiens']"
]
|
null | null | 2406.18892 | null | null | http://arxiv.org/pdf/2406.18892v1 | 2024-06-27T05:08:09Z | 2024-06-27T05:08:09Z | LearnedKV: Integrating LSM and Learned Index for Superior Performance on
SSD | In this paper, we introduce LearnedKV, a novel tiered key-value (KV) store that seamlessly integrates a Log-Structured Merge (LSM) tree with a Learned Index. This integration yields superior read and write performance compared to standalone indexing structures on SSDs. Our design capitalizes on the LSM tree's high write/update throughput and the Learned Index's fast read capabilities, enabling each component to leverage its strengths. We analyze the impact of size on LSM tree performance and demonstrate how the tiered Learned Index significantly mitigates the LSM tree's size-related performance degradation, particularly by reducing the intensive I/O operations resulting from re-insertions after Garbage Collection (GC). To maintain rapid read performance for newly inserted keys, we introduce a non-blocking conversion mechanism that efficiently transforms the existing LSM tree into a new Learned Index with minimal overhead during GC. Our experimental results, conducted across diverse workloads, show that LearnedKV outperforms state-of-the-art solutions by up to 1.32x in read requests and 1.31x in write performance. | [
"['Wenlong Wang' 'David Hung-Chang Du']"
]
|
null | null | 2406.18902 | null | null | http://arxiv.org/pdf/2406.18902v1 | 2024-06-27T05:30:08Z | 2024-06-27T05:30:08Z | Statistical Test for Data Analysis Pipeline by Selective Inference | A data analysis pipeline is a structured sequence of processing steps that transforms raw data into meaningful insights by effectively integrating various analysis algorithms. In this paper, we propose a novel statistical test designed to assess the statistical significance of data analysis pipelines. Our approach allows for the systematic development of valid statistical tests applicable to any data analysis pipeline configuration composed of a set of data analysis components. We have developed this framework by adapting selective inference, which has gained recent attention as a new statistical inference technique for data-driven hypotheses. The proposed statistical test is theoretically designed to control the type I error at the desired significance level in finite samples. As examples, we consider a class of pipelines composed of three missing value imputation algorithms, three outlier detection algorithms, and three feature selection algorithms. We confirm the validity of our statistical test through experiments with both synthetic and real data for this class of data analysis pipelines. Additionally, we present an implementation framework that facilitates testing across any configuration of data analysis pipelines in this class without extra implementation costs. | [
"['Tomohiro Shiraishi' 'Tatsuya Matsukawa' 'Shuichi Nishino'\n 'Ichiro Takeuchi']"
]
|
null | null | 2406.18922 | null | null | http://arxiv.org/pdf/2406.18922v1 | 2024-06-27T06:26:22Z | 2024-06-27T06:26:22Z | Time Matters: Scaling Laws for Any Budget | A primary cost driver for training large models is wall-clock training time. We show that popular time estimates based on FLOPs are poor estimates, and construct a more accurate proxy based on memory copies. We show that with some simple accounting, we can estimate the training speed of a transformer model from its hyperparameters. Combined with a scaling law curve like Chinchilla, this lets us estimate the final loss of the model. We fit our estimate to real data with a linear regression, and apply the result to rewrite Chinchilla in terms of a model's estimated training time as opposed to the amount of training data. This gives an expression for the loss in terms of the model's hyperparameters alone. We show that this expression is accurate across a wide range of model hyperparameter values, enabling us to analytically make architectural decisions and train models more efficiently. | [
"['Itay Inbar' 'Luke Sernau']"
]
|
null | null | 2406.18924 | null | null | http://arxiv.org/pdf/2406.18924v1 | 2024-06-27T06:31:51Z | 2024-06-27T06:31:51Z | Learning Pareto Set for Multi-Objective Continuous Robot Control | For a control problem with multiple conflicting objectives, there exists a set of Pareto-optimal policies called the Pareto set instead of a single optimal policy. When a multi-objective control problem is continuous and complex, traditional multi-objective reinforcement learning (MORL) algorithms search for many Pareto-optimal deep policies to approximate the Pareto set, which is quite resource-consuming. In this paper, we propose a simple and resource-efficient MORL algorithm that learns a continuous representation of the Pareto set in a high-dimensional policy parameter space using a single hypernet. The learned hypernet can directly generate various well-trained policy networks for different user preferences. We compare our method with two state-of-the-art MORL algorithms on seven multi-objective continuous robot control problems. Experimental results show that our method achieves the best overall performance with the least training parameters. An interesting observation is that the Pareto set is well approximated by a curved line or surface in a high-dimensional parameter space. This observation will provide insight for researchers to design new MORL algorithms. | [
"['Tianye Shu' 'Ke Shang' 'Cheng Gong' 'Yang Nan' 'Hisao Ishibuchi']"
]
|
null | null | 2406.18926 | null | null | http://arxiv.org/pdf/2406.18926v1 | 2024-06-27T06:33:41Z | 2024-06-27T06:33:41Z | Fine-tuned network relies on generic representation to solve unseen
cognitive task | Fine-tuning pretrained language models has shown promising results on a wide range of tasks, but when encountering a novel task, do they rely more on generic pretrained representation, or develop brand new task-specific solutions? Here, we fine-tuned GPT-2 on a context-dependent decision-making task, novel to the model but adapted from neuroscience literature. We compared its performance and internal mechanisms to a version of GPT-2 trained from scratch on the same task. Our results show that fine-tuned models depend heavily on pretrained representations, particularly in later layers, while models trained from scratch develop different, more task-specific mechanisms. These findings highlight the advantages and limitations of pretraining for task generalization and underscore the need for further investigation into the mechanisms underpinning task-specific fine-tuning in LLMs. | [
"['Dongyan Lin']"
]
|
null | null | 2406.18928 | null | null | http://arxiv.org/pdf/2406.18928v1 | 2024-06-27T06:40:01Z | 2024-06-27T06:40:01Z | Enhanced ASR Robustness to Packet Loss with a Front-End Adaptation
Network | In the realm of automatic speech recognition (ASR), robustness in noisy environments remains a significant challenge. Recent ASR models, such as Whisper, have shown promise, but their efficacy in noisy conditions can be further enhanced. This study is focused on recovering from packet loss to improve the word error rate (WER) of ASR models. We propose using a front-end adaptation network connected to a frozen ASR model. The adaptation network is trained to modify the corrupted input spectrum by minimizing the criteria of the ASR model in addition to an enhancement loss function. Our experiments demonstrate that the adaptation network, trained on Whisper's criteria, notably reduces word error rates across domains and languages in packet-loss scenarios. This improvement is achieved with minimal affect to Whisper model's foundational performance, underscoring our method's practicality and potential in enhancing ASR models in challenging acoustic environments. | [
"['Yehoshua Dissen' 'Shiry Yonash' 'Israel Cohen' 'Joseph Keshet']"
]
|
null | null | 2406.18931 | null | null | http://arxiv.org/pdf/2406.18931v2 | 2024-07-07T02:02:44Z | 2024-06-27T06:56:46Z | Semi-adaptive Synergetic Two-way Pseudoinverse Learning System | Deep learning has become a crucial technology for making breakthroughs in many fields. Nevertheless, it still faces two important challenges in theoretical and applied aspects. The first lies in the shortcomings of gradient descent based learning schemes which are time-consuming and difficult to determine the learning control hyperparameters. Next, the architectural design of the model is usually tricky. In this paper, we propose a semi-adaptive synergetic two-way pseudoinverse learning system, wherein each subsystem encompasses forward learning, backward learning, and feature concatenation modules. The whole system is trained using a non-gradient descent learning algorithm. It simplifies the hyperparameter tuning while improving the training efficiency. The architecture of the subsystems is designed using a data-driven approach that enables automated determination of the depth of the subsystems. We compare our method with the baselines of mainstream non-gradient descent based methods and the results demonstrate the effectiveness of our proposed method. The source code for this paper is available at http://github.com/B-berrypie/Semi-adaptive-Synergetic-Two-way-Pseudoinverse-Learning-System}{http://github.com/B-berrypie/Semi-adaptive-Synergetic-Two-way-Pseudoinverse-Learning-System. | [
"['Binghong Liu' 'Ziqi Zhao' 'Shupan Li' 'Ke Wang']"
]
|
null | null | 2406.18937 | null | null | http://arxiv.org/pdf/2406.18937v2 | 2024-06-29T18:17:40Z | 2024-06-27T07:08:28Z | Federated Graph Semantic and Structural Learning | Federated graph learning collaboratively learns a global graph neural network with distributed graphs, where the non-independent and identically distributed property is one of the major challenges. Most relative arts focus on traditional distributed tasks like images and voices, incapable of graph structures. This paper firstly reveals that local client distortion is brought by both node-level semantics and graph-level structure. First, for node-level semantics, we find that contrasting nodes from distinct classes is beneficial to provide a well-performing discrimination. We pull the local node towards the global node of the same class and push it away from the global node of different classes. Second, we postulate that a well-structural graph neural network possesses similarity for neighbors due to the inherent adjacency relationships. However, aligning each node with adjacent nodes hinders discrimination due to the potential class inconsistency. We transform the adjacency relationships into the similarity distribution and leverage the global model to distill the relation knowledge into the local model, which preserves the structural information and discriminability of the local model. Empirical results on three graph datasets manifest the superiority of the proposed method over its counterparts. | [
"['Wenke Huang' 'Guancheng Wan' 'Mang Ye' 'Bo Du']"
]
|
null | null | 2406.18939 | null | null | http://arxiv.org/pdf/2406.18939v1 | 2024-06-27T07:11:48Z | 2024-06-27T07:11:48Z | Evaluating AI Group Fairness: a Fuzzy Logic Perspective | Artificial intelligence systems often address fairness concerns by evaluating and mitigating measures of group discrimination, for example that indicate biases against certain genders or races. However, what constitutes group fairness depends on who is asked and the social context, whereas definitions are often relaxed to accept small deviations from the statistical constraints they set out to impose. Here we decouple definitions of group fairness both from the context and from relaxation-related uncertainty by expressing them in the axiomatic system of Basic fuzzy Logic (BL) with loosely understood predicates, like encountering group members. We then evaluate the definitions in subclasses of BL, such as Product or Lukasiewicz logics. Evaluation produces continuous instead of binary truth values by choosing the logic subclass and truth values for predicates that reflect uncertain context-specific beliefs, such as stakeholder opinions gathered through questionnaires. Internally, it follows logic-specific rules to compute the truth values of definitions. We show that commonly held propositions standardize the resulting mathematical formulas and we transcribe logic and truth value choices to layperson terms, so that anyone can answer them. We also use our framework to study several literature definitions of algorithmic fairness, for which we rationalize previous expedient practices that are non-probabilistic and show how to re-interpret their formulas and parameters in new contexts. | [
"['Emmanouil Krasanakis' 'Symeon Papadopoulos']"
]
|
null | null | 2406.18954 | null | null | http://arxiv.org/pdf/2406.18954v1 | 2024-06-27T07:36:25Z | 2024-06-27T07:36:25Z | Alignment For Performance Improvement in Conversation Bots | This paper shows that alignment methods can achieve superior adherence to guardrails compared to instruction fine-tuning alone in conversational agents, also known as bots, within predefined guidelines or 'guardrails'. It examines traditional training approaches such as instruction fine-tuning and the recent advancements in direct alignment methods like Identity Preference Optimization (IPO), and Kahneman-Tversky Optimization (KTO). The effectiveness of alignment techniques both pre and post-instruction tuning is highlighted, illustrating their potential to optimize conversational bots in domains that require strict adherence to specified rules, such as customer care. | [
"['Raghav Garg' 'Kapil Sharma' 'Shrey Singla']"
]
|
null | null | 2406.18990 | null | null | http://arxiv.org/pdf/2406.18990v1 | 2024-06-27T08:29:04Z | 2024-06-27T08:29:04Z | A Fast Learning-Based Surrogate of Electrical Machines using a Reduced
Basis | A surrogate model approximates the outputs of a solver of Partial Differential Equations (PDEs) with a low computational cost. In this article, we propose a method to build learning-based surrogates in the context of parameterized PDEs, which are PDEs that depend on a set of parameters but are also temporal and spatial processes. Our contribution is a method hybridizing the Proper Orthogonal Decomposition and several Support Vector Regression machines. This method is conceived to work in real-time, thus aimed for being used in the context of digital twins, where a user can perform an interactive analysis of results based on the proposed surrogate. We present promising results on two use cases concerning electrical machines. These use cases are not toy examples but are produced an industrial computational code, they use meshes representing non-trivial geometries and contain non-linearities. | [
"['Alejandro Ribés' 'Nawfal Benchekroun' 'Théo Delagnes']"
]
|
null | null | 2406.18992 | null | null | http://arxiv.org/pdf/2406.18992v1 | 2024-06-27T08:33:35Z | 2024-06-27T08:33:35Z | Semi-supervised Concept Bottleneck Models | Concept Bottleneck Models (CBMs) have garnered increasing attention due to their ability to provide concept-based explanations for black-box deep learning models while achieving high final prediction accuracy using human-like concepts. However, the training of current CBMs heavily relies on the accuracy and richness of annotated concepts in the dataset. These concept labels are typically provided by experts, which can be costly and require significant resources and effort. Additionally, concept saliency maps frequently misalign with input saliency maps, causing concept predictions to correspond to irrelevant input features - an issue related to annotation alignment. To address these limitations, we propose a new framework called SSCBM (Semi-supervised Concept Bottleneck Model). Our SSCBM is suitable for practical situations where annotated data is scarce. By leveraging joint training on both labeled and unlabeled data and aligning the unlabeled data at the concept level, we effectively solve these issues. We proposed a strategy to generate pseudo labels and an alignment loss. Experiments demonstrate that our SSCBM is both effective and efficient. With only 20% labeled data, we achieved 93.19% (96.39% in a fully supervised setting) concept accuracy and 75.51% (79.82% in a fully supervised setting) prediction accuracy. | [
"['Lijie Hu' 'Tianhao Huang' 'Huanyi Xie' 'Chenyang Ren' 'Zhengyu Hu'\n 'Lu Yu' 'Di Wang']"
]
|
null | null | 2406.18995 | null | null | http://arxiv.org/pdf/2406.18995v1 | 2024-06-27T08:36:43Z | 2024-06-27T08:36:43Z | FedMLP: Federated Multi-Label Medical Image Classification under Task
Heterogeneity | Cross-silo federated learning (FL) enables decentralized organizations to collaboratively train models while preserving data privacy and has made significant progress in medical image classification. One common assumption is task homogeneity where each client has access to all classes during training. However, in clinical practice, given a multi-label classification task, constrained by the level of medical knowledge and the prevalence of diseases, each institution may diagnose only partial categories, resulting in task heterogeneity. How to pursue effective multi-label medical image classification under task heterogeneity is under-explored. In this paper, we first formulate such a realistic label missing setting in the multi-label FL domain and propose a two-stage method FedMLP to combat class missing from two aspects: pseudo label tagging and global knowledge learning. The former utilizes a warmed-up model to generate class prototypes and select samples with high confidence to supplement missing labels, while the latter uses a global model as a teacher for consistency regularization to prevent forgetting missing class knowledge. Experiments on two publicly-available medical datasets validate the superiority of FedMLP against the state-of-the-art both federated semi-supervised and noisy label learning approaches under task heterogeneity. Code is available at https://github.com/szbonaldo/FedMLP. | [
"['Zhaobin Sun' 'Nannan Wu' 'Junjie Shi' 'Li Yu' 'Xin Yang'\n 'Kwang-Ting Cheng' 'Zengqiang Yan']"
]
|
null | null | 2406.18996 | null | null | http://arxiv.org/pdf/2406.18996v1 | 2024-06-27T08:37:26Z | 2024-06-27T08:37:26Z | Zero-shot domain adaptation based on dual-level mix and contrast | Zero-shot domain adaptation (ZSDA) is a domain adaptation problem in the situation that labeled samples for a target task (task of interest) are only available from the source domain at training time, but for a task different from the task of interest (irrelevant task), labeled samples are available from both source and target domains. In this situation, classical domain adaptation techniques can only learn domain-invariant features in the irrelevant task. However, due to the difference in sample distribution between the two tasks, domain-invariant features learned in the irrelevant task are biased and not necessarily domain-invariant in the task of interest. To solve this problem, this paper proposes a new ZSDA method to learn domain-invariant features with low task bias. To this end, we propose (1) data augmentation with dual-level mixups in both task and domain to fill the absence of target task-of-interest data, (2) an extension of domain adversarial learning to learn domain-invariant features with less task bias, and (3) a new dual-level contrastive learning method that enhances domain-invariance and less task biasedness of features. Experimental results show that our proposal achieves good performance on several benchmarks. | [
"['Yu Zhe' 'Jun Sakuma']"
]
|
null | null | 2406.19015 | null | null | http://arxiv.org/pdf/2406.19015v2 | 2024-07-08T09:07:51Z | 2024-06-27T09:00:05Z | Lithium-Ion Battery System Health Monitoring and Fault Analysis from
Field Data Using Gaussian Processes | Health monitoring, fault analysis, and detection are critical for the safe and sustainable operation of battery systems. We apply Gaussian process resistance models on lithium iron phosphate battery field data to effectively separate the time-dependent and operating point-dependent resistance. The data set contains 29 battery systems returned to the manufacturer for warranty, each with eight cells in series, totaling 232 cells and 131 million data rows. We develop probabilistic fault detection rules using recursive spatiotemporal Gaussian processes. These processes allow the quick processing of over a million data points, enabling advanced online monitoring and furthering the understanding of battery pack failure in the field. The analysis underlines that often, only a single cell shows abnormal behavior or a knee point, consistent with weakest-link failure for cells connected in series, amplified by local resistive heating. The results further the understanding of how batteries degrade and fail in the field and demonstrate the potential of efficient online monitoring based on data. We open-source the code and publish the large data set upon completion of the review of this article. | [
"['Joachim Schaeffer' 'Eric Lenz' 'Duncan Gulla' 'Martin Z. Bazant'\n 'Richard D. Braatz' 'Rolf Findeisen']"
]
|
null | null | 2406.19040 | null | null | http://arxiv.org/pdf/2406.19040v1 | 2024-06-27T09:45:52Z | 2024-06-27T09:45:52Z | On Convex Optimization with Semi-Sensitive Features | We study the differentially private (DP) empirical risk minimization (ERM) problem under the semi-sensitive DP setting where only some features are sensitive. This generalizes the Label DP setting where only the label is sensitive. We give improved upper and lower bounds on the excess risk for DP-ERM. In particular, we show that the error only scales polylogarithmically in terms of the sensitive domain size, improving upon previous results that scale polynomially in the sensitive domain size (Ghazi et al., 2021). | [
"['Badih Ghazi' 'Pritish Kamath' 'Ravi Kumar' 'Pasin Manurangsi'\n 'Raghu Meka' 'Chiyuan Zhang']"
]
|
null | null | 2406.19049 | null | null | http://arxiv.org/pdf/2406.19049v1 | 2024-06-27T09:57:31Z | 2024-06-27T09:57:31Z | Accuracy on the wrong line: On the pitfalls of noisy data for
out-of-distribution generalisation | "Accuracy-on-the-line" is a widely observed phenomenon in machine learning, where a model's accuracy on in-distribution (ID) and out-of-distribution (OOD) data is positively correlated across different hyperparameters and data configurations. But when does this useful relationship break down? In this work, we explore its robustness. The key observation is that noisy data and the presence of nuisance features can be sufficient to shatter the Accuracy-on-the-line phenomenon. In these cases, ID and OOD accuracy can become negatively correlated, leading to "Accuracy-on-the-wrong-line". This phenomenon can also occur in the presence of spurious (shortcut) features, which tend to overshadow the more complex signal (core, non-spurious) features, resulting in a large nuisance feature space. Moreover, scaling to larger datasets does not mitigate this undesirable behavior and may even exacerbate it. We formally prove a lower bound on Out-of-distribution (OOD) error in a linear classification model, characterizing the conditions on the noise and nuisance features for a large OOD error. We finally demonstrate this phenomenon across both synthetic and real datasets with noisy data and nuisance features. | [
"['Amartya Sanyal' 'Yaxi Hu' 'Yaodong Yu' 'Yian Ma' 'Yixin Wang'\n 'Bernhard Schölkopf']"
]
|
null | null | 2406.19050 | null | null | http://arxiv.org/pdf/2406.19050v1 | 2024-06-27T09:58:43Z | 2024-06-27T09:58:43Z | FedMap: Iterative Magnitude-Based Pruning for Communication-Efficient
Federated Learning | Federated Learning (FL) is a distributed machine learning approach that enables training on decentralized data while preserving privacy. However, FL systems often involve resource-constrained client devices with limited computational power, memory, storage, and bandwidth. This paper introduces FedMap, a novel method that aims to enhance the communication efficiency of FL deployments by collaboratively learning an increasingly sparse global model through iterative, unstructured pruning. Importantly, FedMap trains a global model from scratch, unlike other methods reported in the literature, making it ideal for privacy-critical use cases such as in the medical and finance domains, where suitable pre-training data is often limited. FedMap adapts iterative magnitude-based pruning to the FL setting, ensuring all clients prune and refine the same subset of the global model parameters, therefore gradually reducing the global model size and communication overhead. The iterative nature of FedMap, forming subsequent models as subsets of predecessors, avoids parameter reactivation issues seen in prior work, resulting in stable performance. In this paper we provide an extensive evaluation of FedMap across diverse settings, datasets, model architectures, and hyperparameters, assessing performance in both IID and non-IID environments. Comparative analysis against the baseline approach demonstrates FedMap's ability to achieve more stable client model performance. For IID scenarios, FedMap achieves over $90$% pruning without significant performance degradation. In non-IID settings, it achieves at least $~80$% pruning while maintaining accuracy. FedMap offers a promising solution to alleviate communication bottlenecks in FL systems while retaining model accuracy. | [
"['Alexander Herzog' 'Robbie Southam' 'Ioannis Mavromatis' 'Aftab Khan']"
]
|
null | null | 2406.19051 | null | null | http://arxiv.org/pdf/2406.19051v1 | 2024-06-27T09:59:28Z | 2024-06-27T09:59:28Z | Stochastic Gradient Piecewise Deterministic Monte Carlo Samplers | Recent work has suggested using Monte Carlo methods based on piecewise deterministic Markov processes (PDMPs) to sample from target distributions of interest. PDMPs are non-reversible continuous-time processes endowed with momentum, and hence can mix better than standard reversible MCMC samplers. Furthermore, they can incorporate exact sub-sampling schemes which only require access to a single (randomly selected) data point at each iteration, yet without introducing bias to the algorithm's stationary distribution. However, the range of models for which PDMPs can be used, particularly with sub-sampling, is limited. We propose approximate simulation of PDMPs with sub-sampling for scalable sampling from posterior distributions. The approximation takes the form of an Euler approximation to the true PDMP dynamics, and involves using an estimate of the gradient of the log-posterior based on a data sub-sample. We thus call this class of algorithms stochastic-gradient PDMPs. Importantly, the trajectories of stochastic-gradient PDMPs are continuous and can leverage recent ideas for sampling from measures with continuous and atomic components. We show these methods are easy to implement, present results on their approximation error and demonstrate numerically that this class of algorithms has similar efficiency to, but is more robust than, stochastic gradient Langevin dynamics. | [
"['Paul Fearnhead' 'Sebastiano Grazzi' 'Chris Nemeth' 'Gareth O. Roberts']"
]
|
null | null | 2406.19054 | null | null | http://arxiv.org/pdf/2406.19054v1 | 2024-06-27T10:01:56Z | 2024-06-27T10:01:56Z | A look under the hood of the Interactive Deep Learning Enterprise
(No-IDLE) | This DFKI technical report presents the anatomy of the No-IDLE prototype system (funded by the German Federal Ministry of Education and Research) that provides not only basic and fundamental research in interactive machine learning, but also reveals deeper insights into users' behaviours, needs, and goals. Machine learning and deep learning should become accessible to millions of end users. No-IDLE's goals and scienfific challenges centre around the desire to increase the reach of interactive deep learning solutions for non-experts in machine learning. One of the key innovations described in this technical report is a methodology for interactive machine learning combined with multimodal interaction which will become central when we start interacting with semi-intelligent machines in the upcoming area of neural networks and large language models. | [
"['Daniel Sonntag' 'Michael Barz' 'Thiago Gouvêa']"
]
|
null | null | 2406.19057 | null | null | http://arxiv.org/pdf/2406.19057v2 | 2024-06-30T07:54:30Z | 2024-06-27T10:08:29Z | Segment Anything Model for automated image data annotation: empirical
studies using text prompts from Grounding DINO | Grounding DINO and the Segment Anything Model (SAM) have achieved impressive performance in zero-shot object detection and image segmentation, respectively. Together, they have a great potential to revolutionize applications in zero-shot semantic segmentation or data annotation. Yet, in specialized domains like medical image segmentation, objects of interest (e.g., organs, tissues, and tumors) may not fall in existing class names. To address this problem, the referring expression comprehension (REC) ability of Grounding DINO is leveraged to detect arbitrary targets by their language descriptions. However, recent studies have highlighted severe limitation of the REC framework in this application setting owing to its tendency to make false positive predictions when the target is absent in the given image. And, while this bottleneck is central to the prospect of open-set semantic segmentation, it is still largely unknown how much improvement can be achieved by studying the prediction errors. To this end, we perform empirical studies on six publicly available datasets across different domains and reveal that these errors consistently follow a predictable pattern and can, thus, be mitigated by a simple strategy. Specifically, we show that false positive detections with appreciable confidence scores generally occupy large image areas and can usually be filtered by their relative sizes. More importantly, we expect these observations to inspire future research in improving REC-based detection and automated segmentation. Meanwhile, we evaluate the performance of SAM on multiple datasets from various specialized domains and report significant improvements in segmentation performance and annotation time savings over manual approaches. | [
"['Fuseini Mumuni' 'Alhassan Mumuni']"
]
|
null | null | 2406.19066 | null | null | http://arxiv.org/pdf/2406.19066v1 | 2024-06-27T10:34:50Z | 2024-06-27T10:34:50Z | Dancing in the Shadows: Harnessing Ambiguity for Fairer Classifiers | This paper introduces a novel approach to bolster algorithmic fairness in scenarios where sensitive information is only partially known. In particular, we propose to leverage instances with uncertain identity with regards to the sensitive attribute to train a conventional machine learning classifier. The enhanced fairness observed in the final predictions of this classifier highlights the promising potential of prioritizing ambiguity (i.e., non-normativity) as a means to improve fairness guarantees in real-world classification tasks. | [
"['Ainhize Barrainkua' 'Paula Gordaliza' 'Jose A. Lozano' 'Novi Quadrianto']"
]
|
null | null | 2406.19087 | null | null | http://arxiv.org/pdf/2406.19087v1 | 2024-06-27T11:14:14Z | 2024-06-27T11:14:14Z | Dimensions underlying the representational alignment of deep neural
networks with humans | Determining the similarities and differences between humans and artificial intelligence is an important goal both in machine learning and cognitive neuroscience. However, similarities in representations only inform us about the degree of alignment, not the factors that determine it. Drawing upon recent developments in cognitive science, we propose a generic framework for yielding comparable representations in humans and deep neural networks (DNN). Applying this framework to humans and a DNN model of natural images revealed a low-dimensional DNN embedding of both visual and semantic dimensions. In contrast to humans, DNNs exhibited a clear dominance of visual over semantic features, indicating divergent strategies for representing images. While in-silico experiments showed seemingly-consistent interpretability of DNN dimensions, a direct comparison between human and DNN representations revealed substantial differences in how they process images. By making representations directly comparable, our results reveal important challenges for representational alignment, offering a means for improving their comparability. | [
"['Florian P. Mahner' 'Lukas Muttenthaler' 'Umut Güçlü' 'Martin N. Hebart']"
]
|
null | null | 2406.19092 | null | null | http://arxiv.org/pdf/2406.19092v1 | 2024-06-27T11:17:13Z | 2024-06-27T11:17:13Z | Adaptive Stochastic Weight Averaging | Ensemble models often improve generalization performances in challenging tasks. Yet, traditional techniques based on prediction averaging incur three well-known disadvantages: the computational overhead of training multiple models, increased latency, and memory requirements at test time. To address these issues, the Stochastic Weight Averaging (SWA) technique maintains a running average of model parameters from a specific epoch onward. Despite its potential benefits, maintaining a running average of parameters can hinder generalization, as an underlying running model begins to overfit. Conversely, an inadequately chosen starting point can render SWA more susceptible to underfitting compared to an underlying running model. In this work, we propose Adaptive Stochastic Weight Averaging (ASWA) technique that updates a running average of model parameters, only when generalization performance is improved on the validation dataset. Hence, ASWA can be seen as a combination of SWA with the early stopping technique, where the former accepts all updates on a parameter ensemble model and the latter rejects any update on an underlying running model. We conducted extensive experiments ranging from image classification to multi-hop reasoning over knowledge graphs. Our experiments over 11 benchmark datasets with 7 baseline models suggest that ASWA leads to a statistically better generalization across models and datasets | [
"['Caglar Demir' 'Arnab Sharma' 'Axel-Cyrille Ngonga Ngomo']"
]
|
null | null | 2406.19112 | null | null | http://arxiv.org/pdf/2406.19112v1 | 2024-06-27T11:48:25Z | 2024-06-27T11:48:25Z | A Teacher Is Worth A Million Instructions | Large Language Models(LLMs) have shown exceptional abilities, yet training these models can be quite challenging. There is a strong dependence on the quality of data and finding the best instruction tuning set. Further, the inherent limitations in training methods create substantial difficulties to train relatively smaller models with 7B and 13B parameters. In our research, we suggest an improved training method for these models by utilising knowledge from larger models, such as a mixture of experts (8x7B) architectures. The scale of these larger models allows them to capture a wide range of variations from data alone, making them effective teachers for smaller models. Moreover, we implement a novel post-training domain alignment phase that employs domain-specific expert models to boost domain-specific knowledge during training while preserving the model's ability to generalise. Fine-tuning Mistral 7B and 2x7B with our method surpasses the performance of state-of-the-art language models with more than 7B and 13B parameters: achieving up to $7.9$ in MT-Bench and $93.04%$ on AlpacaEval. | [
"['Nikhil Kothari' 'Ravindra Nayak' 'Shreyas Shetty' 'Amey Patil'\n 'Nikesh Garera']"
]
|
null | null | 2406.19116 | null | null | http://arxiv.org/pdf/2406.19116v1 | 2024-06-27T11:53:15Z | 2024-06-27T11:53:15Z | CHEW: A Dataset of CHanging Events in Wikipedia | We introduce CHEW, a novel dataset of changing events in Wikipedia expressed in naturally occurring text. We use CHEW for probing LLMs for their timeline understanding of Wikipedia entities and events in generative and classification experiments. Our results suggest that LLMs, despite having temporal information available, struggle to construct accurate timelines. We further show the usefulness of CHEW-derived embeddings for identifying meaning shift. | [
"['Hsuvas Borkakoty' 'Luis Espinosa-Anke']"
]
|
null | null | 2406.19121 | null | null | http://arxiv.org/pdf/2406.19121v1 | 2024-06-27T12:05:55Z | 2024-06-27T12:05:55Z | Towards Learning Abductive Reasoning using VSA Distributed
Representations | We introduce the Abductive Rule Learner with Context-awareness (ARLC), a model that solves abstract reasoning tasks based on Learn-VRF. ARLC features a novel and more broadly applicable training objective for abductive reasoning, resulting in better interpretability and higher accuracy when solving Raven's progressive matrices (RPM). ARLC allows both programming domain knowledge and learning the rules underlying a data distribution. We evaluate ARLC on the I-RAVEN dataset, showcasing state-of-the-art accuracy across both in-distribution and out-of-distribution (unseen attribute-rule pairs) tests. ARLC surpasses neuro-symbolic and connectionist baselines, including large language models, despite having orders of magnitude fewer parameters. We show ARLC's robustness to post-programming training by incrementally learning from examples on top of programmed knowledge, which only improves its performance and does not result in catastrophic forgetting of the programmed solution. We validate ARLC's seamless transfer learning from a 2x2 RPM constellation to unseen constellations. Our code is available at https://github.com/IBM/abductive-rule-learner-with-context-awareness. | [
"['Giacomo Camposampiero' 'Michael Hersche' 'Aleksandar Terzić'\n 'Roger Wattenhofer' 'Abu Sebastian' 'Abbas Rahimi']"
]
|
null | null | 2406.19136 | null | null | http://arxiv.org/pdf/2406.19136v3 | 2024-07-07T14:10:38Z | 2024-06-27T12:40:29Z | YZS-model: A Predictive Model for Organic Drug Solubility Based on Graph
Convolutional Networks and Transformer-Attention | The accurate prediction of drug molecule solubility is essential for determining their therapeutic effectiveness and safety, influencing the drug's ADME processes. Traditional solubility prediction techniques often fail to capture the complex nature of molecular tructures, leading to notable deviations between predictions and actual results. For example, the Discussion on Advanced Drug-Like Compound Structures. Lusci highlighted issues in capturing crucial cyclic structural information in molecules with ring structures. To overcome this issue, our research introduces a novel deep learning framework combining attention-based transformers, Long Short-Term Memory (LSTM) networks, and Graph Convolutional Networks (GCN), aimed at enhancing the precision of solubility predictions. Utilizing a training set of 9,943 compounds and testing on an anticancer compound dataset, our method achieved a correlation coefficient ($R^2$) of 0.59 and a Root Mean Square Error (RMSE) of 0.57, which outperforms the benchmark models' scores of 0.52 ($R^2$) and 0.61 (RMSE). Importantly, in an additional independent test, our model significantly outperformed the baseline with an RMSE of 1.05 compared to 1.28, a relative accuracy improvement of 45.9%. This research not only demonstrates the vast potential of deep learning for improving solubility prediction accuracy but also offers novel insights for drug design and selection in the future. Continued efforts will be directed towards optimizing the model architecture and extending its application to better support the drug development process, underscoring the pivotal role of deep learning in drug discovery. | [
"['Chenxu Wang' 'Haowei Ming' 'Jian He' 'Yao Lu']"
]
|
null | null | 2406.19146 | null | null | http://arxiv.org/pdf/2406.19146v1 | 2024-06-27T13:02:43Z | 2024-06-27T13:02:43Z | Resolving Discrepancies in Compute-Optimal Scaling of Language Models | Kaplan et al. and Hoffmann et al. developed influential scaling laws for the optimal model size as a function of the compute budget, but these laws yield substantially different predictions. We explain the discrepancy by reproducing the Kaplan scaling law on two datasets (OpenWebText2 and RefinedWeb) and identifying three factors causing the difference: last layer computational cost, warmup duration, and scale-dependent optimizer tuning. With these factors corrected, we obtain excellent agreement with the Hoffmann et al. (i.e., "Chinchilla") scaling law. Counter to a hypothesis of Hoffmann et al., we find that careful learning rate decay is not essential for the validity of their scaling law. As a secondary result, we derive scaling laws for the optimal learning rate and batch size, finding that tuning the AdamW $beta_2$ parameter is essential at lower batch sizes. | [
"['Tomer Porian' 'Mitchell Wortsman' 'Jenia Jitsev' 'Ludwig Schmidt'\n 'Yair Carmon']"
]
|
null | null | 2406.19154 | null | null | http://arxiv.org/pdf/2406.19154v1 | 2024-06-27T13:14:20Z | 2024-06-27T13:14:20Z | Advancing operational PM2.5 forecasting with dual deep neural networks
(D-DNet) | PM2.5 forecasting is crucial for public health, air quality management, and policy development. Traditional physics-based models are computationally demanding and slow to adapt to real-time conditions. Deep learning models show potential in efficiency but still suffer from accuracy loss over time due to error accumulation. To address these challenges, we propose a dual deep neural network (D-DNet) prediction and data assimilation system that efficiently integrates real-time observations, ensuring reliable operational forecasting. D-DNet excels in global operational forecasting for PM2.5 and AOD550, maintaining consistent accuracy throughout the entire year of 2019. It demonstrates notably higher efficiency than the Copernicus Atmosphere Monitoring Service (CAMS) 4D-Var operational forecasting system while maintaining comparable accuracy. This efficiency benefits ensemble forecasting, uncertainty analysis, and large-scale tasks. | [
"['Shengjuan Cai' 'Fangxin Fang' 'Vincent-Henri Peuch' 'Mihai Alexe'\n 'Ionel Michael Navon' 'Yanghua Wang']"
]
|
null | null | 2406.19156 | null | null | http://arxiv.org/pdf/2406.19156v1 | 2024-06-27T13:17:33Z | 2024-06-27T13:17:33Z | Heterogeneous Causal Metapath Graph Neural Network for
Gene-Microbe-Disease Association Prediction | The recent focus on microbes in human medicine highlights their potential role in the genetic framework of diseases. To decode the complex interactions among genes, microbes, and diseases, computational predictions of gene-microbe-disease (GMD) associations are crucial. Existing methods primarily address gene-disease and microbe-disease associations, but the more intricate triple-wise GMD associations remain less explored. In this paper, we propose a Heterogeneous Causal Metapath Graph Neural Network (HCMGNN) to predict GMD associations. HCMGNN constructs a heterogeneous graph linking genes, microbes, and diseases through their pairwise associations, and utilizes six predefined causal metapaths to extract directed causal subgraphs, which facilitate the multi-view analysis of causal relations among three entity types. Within each subgraph, we employ a causal semantic sharing message passing network for node representation learning, coupled with an attentive fusion method to integrate these representations for predicting GMD associations. Our extensive experiments show that HCMGNN effectively predicts GMD associations and addresses association sparsity issue by enhancing the graph's semantics and structure. | [
"['Kexin Zhang' 'Feng Huang' 'Luotao Liu' 'Zhankun Xiong' 'Hongyu Zhang'\n 'Yuan Quan' 'Wen Zhang']"
]
|
null | null | 2406.19175 | null | null | http://arxiv.org/pdf/2406.19175v1 | 2024-06-27T13:51:53Z | 2024-06-27T13:51:53Z | Towards Reducing Data Acquisition and Labeling for Defect Detection
using Simulated Data | In many manufacturing settings, annotating data for machine learning and computer vision is costly, but synthetic data can be generated at significantly lower cost. Substituting the real-world data with synthetic data is therefore appealing for many machine learning applications that require large amounts of training data. However, relying solely on synthetic data is frequently inadequate for effectively training models that perform well on real-world data, primarily due to domain shifts between the synthetic and real-world data. We discuss approaches for dealing with such a domain shift when detecting defects in X-ray scans of aluminium wheels. Using both simulated and real-world X-ray images, we train an object detection model with different strategies to identify the training approach that generates the best detection results while minimising the demand for annotated real-world training samples. Our preliminary findings suggest that the sim-2-real domain adaptation approach is more cost-efficient than a fully supervised oracle - if the total number of available annotated samples is fixed. Given a certain number of labeled real-world samples, training on a mix of synthetic and unlabeled real-world data achieved comparable or even better detection results at significantly lower cost. We argue that future research into the cost-efficiency of different training strategies is important for a better understanding of how to allocate budget in applied machine learning projects. | [
"['Lukas Malte Kemeter' 'Rasmus Hvingelby' 'Paulina Sierak' 'Tobias Schön'\n 'Bishwajit Gosswam']"
]
|
null | null | 2406.19185 | null | null | http://arxiv.org/pdf/2406.19185v1 | 2024-06-27T14:03:49Z | 2024-06-27T14:03:49Z | Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a
supervised-friendly fashion | Reinforcement Learning (RL) has been used to finetune Large Language Models (LLMs) using a reward model trained from preference data, to better align with human judgment. The recently introduced direct alignment methods, which are often simpler, more stable, and computationally lighter, can more directly achieve this. However, these approaches cannot optimize arbitrary rewards, and the preference-based ones are not the only rewards of interest for LLMs (eg., unit tests for code generation or textual entailment for summarization, among others). RL-finetuning is usually done with a variation of policy gradient, which calls for on-policy or near-on-policy samples, requiring costly generations. We introduce Contrastive Policy Gradient, or CoPG, a simple and mathematically principled new RL algorithm that can estimate the optimal policy even from off-policy data. It can be seen as an off-policy policy gradient approach that does not rely on important sampling techniques and highlights the importance of using (the right) state baseline. We show this approach to generalize the direct alignment method IPO (identity preference optimization) and classic policy gradient. We experiment with the proposed CoPG on a toy bandit problem to illustrate its properties, as well as for finetuning LLMs on a summarization task, using a learned reward function considered as ground truth for the purpose of the experiments. | [
"['Yannis Flet-Berliac' 'Nathan Grinsztajn' 'Florian Strub' 'Eugene Choi'\n 'Chris Cremer' 'Arash Ahmadian' 'Yash Chandak' 'Mohammad Gheshlaghi Azar'\n 'Olivier Pietquin' 'Matthieu Geist']"
]
|
null | null | 2406.19188 | null | null | http://arxiv.org/pdf/2406.19188v1 | 2024-06-27T14:07:38Z | 2024-06-27T14:07:38Z | Averaging log-likelihoods in direct alignment | To better align Large Language Models (LLMs) with human judgment, Reinforcement Learning from Human Feedback (RLHF) learns a reward model and then optimizes it using regularized RL. Recently, direct alignment methods were introduced to learn such a fine-tuned model directly from a preference dataset without computing a proxy reward function. These methods are built upon contrastive losses involving the log-likelihood of (dis)preferred completions according to the trained model. However, completions have various lengths, and the log-likelihood is not length-invariant. On the other side, the cross-entropy loss used in supervised training is length-invariant, as batches are typically averaged token-wise. To reconcile these approaches, we introduce a principled approach for making direct alignment length-invariant. Formally, we introduce a new averaging operator, to be composed with the optimality operator giving the best policy for the underlying RL problem. It translates into averaging the log-likelihood within the loss. We empirically study the effect of such averaging, observing a trade-off between the length of generations and their scores. | [
"['Nathan Grinsztajn' 'Yannis Flet-Berliac' 'Mohammad Gheshlaghi Azar'\n 'Florian Strub' 'Bill Wu' 'Eugene Choi' 'Chris Cremer' 'Arash Ahmadian'\n 'Yash Chandak' 'Olivier Pietquin' 'Matthieu Geist']"
]
|
null | null | 2406.19189 | null | null | http://arxiv.org/pdf/2406.19189v1 | 2024-06-27T14:09:10Z | 2024-06-27T14:09:10Z | BISeizuRe: BERT-Inspired Seizure Data Representation to Improve Epilepsy
Monitoring | This study presents a novel approach for EEG-based seizure detection leveraging a BERT-based model. The model, BENDR, undergoes a two-phase training process. Initially, it is pre-trained on the extensive Temple University Hospital EEG Corpus (TUEG), a 1.5 TB dataset comprising over 10,000 subjects, to extract common EEG data patterns. Subsequently, the model is fine-tuned on the CHB-MIT Scalp EEG Database, consisting of 664 EEG recordings from 24 pediatric patients, of which 198 contain seizure events. Key contributions include optimizing fine-tuning on the CHB-MIT dataset, where the impact of model architecture, pre-processing, and post-processing techniques are thoroughly examined to enhance sensitivity and reduce false positives per hour (FP/h). We also explored custom training strategies to ascertain the most effective setup. The model undergoes a novel second pre-training phase before subject-specific fine-tuning, enhancing its generalization capabilities. The optimized model demonstrates substantial performance enhancements, achieving as low as 0.23 FP/h, 2.5$times$ lower than the baseline model, with a lower but still acceptable sensitivity rate, showcasing the effectiveness of applying a BERT-based approach on EEG-based seizure detection. | [
"['Luca Benfenati' 'Thorir Mar Ingolfsson' 'Andrea Cossettini'\n 'Daniele Jahier Pagliari' 'Alessio Burrello' 'Luca Benini']"
]
|
null | null | 2406.19195 | null | null | http://arxiv.org/pdf/2406.19195v1 | 2024-06-27T14:13:46Z | 2024-06-27T14:13:46Z | Estimating Long-term Heterogeneous Dose-response Curve: Generalization
Bound Leveraging Optimal Transport Weights | Long-term causal effect estimation is a significant but challenging problem in many applications. Existing methods rely on ideal assumptions to estimate long-term average effects, e.g., no unobserved confounders or a binary treatment,while in numerous real-world applications, these assumptions could be violated and average effects are unable to provide individual-level suggestions.In this paper,we address a more general problem of estimating the long-term heterogeneous dose-response curve (HDRC) while accounting for unobserved confounders. Specifically, to remove unobserved confounding in observational data, we introduce an optimal transport weighting framework to align the observational data to the experimental data with theoretical guarantees. Furthermore,to accurately predict the heterogeneous effects of continuous treatment, we establish a generalization bound on counterfactual prediction error by leveraging the reweighted distribution induced by optimal transport. Finally, we develop an HDRC estimator building upon the above theoretical foundations. Extensive experimental studies conducted on multiple synthetic and semi-synthetic datasets demonstrate the effectiveness of our proposed method. | [
"['Zeqin Yang' 'Weilin Chen' 'Ruichu Cai' 'Yuguang Yan' 'Zhifeng Hao'\n 'Zhipeng Yu' 'Zhichao Zou' 'Zhen Peng' 'Jiecheng Guo']"
]
|
null | null | 2406.19223 | null | null | http://arxiv.org/pdf/2406.19223v1 | 2024-06-27T14:49:08Z | 2024-06-27T14:49:08Z | T-FREE: Tokenizer-Free Generative LLMs via Sparse Representations for
Memory-Efficient Embeddings | Tokenizers are crucial for encoding information in Large Language Models, but their development has recently stagnated, and they contain inherent weaknesses. Major limitations include computational overhead, ineffective vocabulary use, and unnecessarily large embedding and head layers. Additionally, their performance is biased towards a reference corpus, leading to reduced effectiveness for underrepresented languages. To remedy these issues, we propose T-FREE, which directly embeds words through sparse activation patterns over character triplets, and does not require a reference corpus. T-FREE inherently exploits morphological similarities and allows for strong compression of embedding layers. In our exhaustive experimental evaluation, we achieve competitive downstream performance with a parameter reduction of more than 85% on these layers. Further, T-FREE shows significant improvements in cross-lingual transfer learning. | [
"['Björn Deiseroth' 'Manuel Brack' 'Patrick Schramowski'\n 'Kristian Kersting' 'Samuel Weinbach']"
]
|
null | null | 2406.19228 | null | null | http://arxiv.org/pdf/2406.19228v1 | 2024-06-27T14:52:34Z | 2024-06-27T14:52:34Z | Tools Fail: Detecting Silent Errors in Faulty Tools | Tools have become a mainstay of LLMs, allowing them to retrieve knowledge not in their weights, to perform tasks on the web, and even to control robots. However, most ontologies and surveys of tool-use have assumed the core challenge for LLMs is choosing the tool. Instead, we introduce a framework for tools more broadly which guides us to explore a model's ability to detect "silent" tool errors, and reflect on how to plan. This more directly aligns with the increasingly popular use of models as tools. We provide an initial approach to failure recovery with promising results both on a controlled calculator setting and embodied agent planning. | [
"['Jimin Sun' 'So Yeon Min' 'Yingshan Chang' 'Yonatan Bisk']"
]
|
null | null | 2406.19237 | null | null | http://arxiv.org/pdf/2406.19237v2 | 2024-06-28T05:43:46Z | 2024-06-27T15:01:48Z | FlowVQA: Mapping Multimodal Logic in Visual Question Answering with
Flowcharts | Existing benchmarks for visual question answering lack in visual grounding and complexity, particularly in evaluating spatial reasoning skills. We introduce FlowVQA, a novel benchmark aimed at assessing the capabilities of visual question-answering multimodal language models in reasoning with flowcharts as visual contexts. FlowVQA comprises 2,272 carefully generated and human-verified flowchart images from three distinct content sources, along with 22,413 diverse question-answer pairs, to test a spectrum of reasoning tasks, including information localization, decision-making, and logical progression. We conduct a thorough baseline evaluation on a suite of both open-source and proprietary multimodal language models using various strategies, followed by an analysis of directional bias. The results underscore the benchmark's potential as a vital tool for advancing the field of multimodal modeling, providing a focused and challenging environment for enhancing model performance in visual and logical reasoning tasks. | [
"['Shubhankar Singh' 'Purvi Chaurasia' 'Yerram Varun' 'Pranshu Pandya'\n 'Vatsal Gupta' 'Vivek Gupta' 'Dan Roth']"
]
|
null | null | 2406.19238 | null | null | http://arxiv.org/pdf/2406.19238v1 | 2024-06-27T15:01:53Z | 2024-06-27T15:01:53Z | Revealing Fine-Grained Values and Opinions in Large Language Models | Uncovering latent values and opinions in large language models (LLMs) can help identify biases and mitigate potential harm. Recently, this has been approached by presenting LLMs with survey questions and quantifying their stances towards morally and politically charged statements. However, the stances generated by LLMs can vary greatly depending on how they are prompted, and there are many ways to argue for or against a given position. In this work, we propose to address this by analysing a large and robust dataset of 156k LLM responses to the 62 propositions of the Political Compass Test (PCT) generated by 6 LLMs using 420 prompt variations. We perform coarse-grained analysis of their generated stances and fine-grained analysis of the plain text justifications for those stances. For fine-grained analysis, we propose to identify tropes in the responses: semantically similar phrases that are recurrent and consistent across different prompts, revealing patterns in the text that a given LLM is prone to produce. We find that demographic features added to prompts significantly affect outcomes on the PCT, reflecting bias, as well as disparities between the results of tests when eliciting closed-form vs. open domain responses. Additionally, patterns in the plain text rationales via tropes show that similar justifications are repeatedly generated across models and prompts even with disparate stances. | [
"['Dustin Wright' 'Arnav Arora' 'Nadav Borenstein' 'Srishti Yadav'\n 'Serge Belongie' 'Isabelle Augenstein']"
]
|
null | null | 2406.19244 | null | null | http://arxiv.org/abs/2406.19244v1 | 2024-06-27T15:10:56Z | 2024-06-27T15:10:56Z | Improving the Expressiveness of $K$-hop Message-Passing GNNs by
Injecting Contextualized Substructure Information | Graph neural networks (GNNs) have become the textit{de facto} standard for representational learning in graphs, and have achieved state-of-the-art performance in many graph-related tasks; however, it has been shown that the expressive power of standard GNNs are equivalent maximally to 1-dimensional Weisfeiler-Lehman (1-WL) Test. Recently, there is a line of works aiming to enhance the expressive power of graph neural networks. One line of such works aim at developing $K$-hop message-passing GNNs where node representation is updated by aggregating information from not only direct neighbors but all neighbors within $K$-hop of the node. Another line of works leverages subgraph information to enhance the expressive power which is proven to be strictly more powerful than 1-WL test. In this work, we discuss the limitation of $K$-hop message-passing GNNs and propose textit{substructure encoding function} to uplift the expressive power of any $K$-hop message-passing GNN. We further inject contextualized substructure information to enhance the expressiveness of $K$-hop message-passing GNNs. Our method is provably more powerful than previous works on $K$-hop graph neural networks and 1-WL subgraph GNNs, which is a specific type of subgraph based GNN models, and not less powerful than 3-WL. Empirically, our proposed method set new state-of-the-art performance or achieves comparable performance for a variety of datasets. Our code is available at url{https://github.com/tianyao-aka/Expresive_K_hop_GNNs}. | [
"['Tianjun Yao' 'Yiongxu Wang' 'Kun Zhang' 'Shangsong Liang']"
]
|
null | null | 2406.19249 | null | null | http://arxiv.org/pdf/2406.19249v1 | 2024-06-27T15:16:00Z | 2024-06-27T15:16:00Z | NTFormer: A Composite Node Tokenized Graph Transformer for Node
Classification | Recently, the emerging graph Transformers have made significant advancements for node classification on graphs. In most graph Transformers, a crucial step involves transforming the input graph into token sequences as the model input, enabling Transformer to effectively learn the node representations. However, we observe that existing methods only express partial graph information of nodes through single-type token generation. Consequently, they require tailored strategies to encode additional graph-specific features into the Transformer to ensure the quality of node representation learning, limiting the model flexibility to handle diverse graphs. To this end, we propose a new graph Transformer called NTFormer to address this issue. NTFormer introduces a novel token generator called Node2Par, which constructs various token sequences using different token elements for each node. This flexibility allows Node2Par to generate valuable token sequences from different perspectives, ensuring comprehensive expression of rich graph features. Benefiting from the merits of Node2Par, NTFormer only leverages a Transformer-based backbone without graph-specific modifications to learn node representations, eliminating the need for graph-specific modifications. Extensive experiments conducted on various benchmark datasets containing homophily and heterophily graphs with different scales demonstrate the superiority of NTFormer over representative graph Transformers and graph neural networks for node classification. | [
"['Jinsong Chen' 'Siyu Jiang' 'Kun He']"
]
|
null | null | 2406.19253 | null | null | http://arxiv.org/pdf/2406.19253v1 | 2024-06-27T15:22:21Z | 2024-06-27T15:22:21Z | Advection Augmented Convolutional Neural Networks | Many problems in physical sciences are characterized by the prediction of space-time sequences. Such problems range from weather prediction to the analysis of disease propagation and video prediction. Modern techniques for the solution of these problems typically combine Convolution Neural Networks (CNN) architecture with a time prediction mechanism. However, oftentimes, such approaches underperform in the long-range propagation of information and lack explainability. In this work, we introduce a physically inspired architecture for the solution of such problems. Namely, we propose to augment CNNs with advection by designing a novel semi-Lagrangian push operator. We show that the proposed operator allows for the non-local transformation of information compared with standard convolutional kernels. We then complement it with Reaction and Diffusion neural components to form a network that mimics the Reaction-Advection-Diffusion equation, in high dimensions. We demonstrate the effectiveness of our network on a number of spatio-temporal datasets that show their merit. | [
"['Niloufar Zakariaei' 'Siddharth Rout' 'Eldad Haber' 'Moshe Eliasof']"
]
|
null | null | 2406.19258 | null | null | http://arxiv.org/pdf/2406.19258v1 | 2024-06-27T15:29:47Z | 2024-06-27T15:29:47Z | Leveraging Contrastive Learning for Enhanced Node Representations in
Tokenized Graph Transformers | While tokenized graph Transformers have demonstrated strong performance in node classification tasks, their reliance on a limited subset of nodes with high similarity scores for constructing token sequences overlooks valuable information from other nodes, hindering their ability to fully harness graph information for learning optimal node representations. To address this limitation, we propose a novel graph Transformer called GCFormer. Unlike previous approaches, GCFormer develops a hybrid token generator to create two types of token sequences, positive and negative, to capture diverse graph information. And a tailored Transformer-based backbone is adopted to learn meaningful node representations from these generated token sequences. Additionally, GCFormer introduces contrastive learning to extract valuable information from both positive and negative token sequences, enhancing the quality of learned node representations. Extensive experimental results across various datasets, including homophily and heterophily graphs, demonstrate the superiority of GCFormer in node classification, when compared to representative graph neural networks (GNNs) and graph Transformers. | [
"['Jinsong Chen' 'Hanpeng Liu' 'John E. Hopcroft' 'Kun He']"
]
|
null | null | 2406.19272 | null | null | http://arxiv.org/pdf/2406.19272v1 | 2024-06-27T15:38:37Z | 2024-06-27T15:38:37Z | Stochastic Concept Bottleneck Models | Concept Bottleneck Models (CBMs) have emerged as a promising interpretable method whose final prediction is based on intermediate, human-understandable concepts rather than the raw input. Through time-consuming manual interventions, a user can correct wrongly predicted concept values to enhance the model's downstream performance. We propose Stochastic Concept Bottleneck Models (SCBMs), a novel approach that models concept dependencies. In SCBMs, a single-concept intervention affects all correlated concepts, thereby improving intervention effectiveness. Unlike previous approaches that model the concept relations via an autoregressive structure, we introduce an explicit, distributional parameterization that allows SCBMs to retain the CBMs' efficient training and inference procedure. Additionally, we leverage the parameterization to derive an effective intervention strategy based on the confidence region. We show empirically on synthetic tabular and natural image datasets that our approach improves intervention effectiveness significantly. Notably, we showcase the versatility and usability of SCBMs by examining a setting with CLIP-inferred concepts, alleviating the need for manual concept annotations. | [
"['Moritz Vandenhirtz' 'Sonia Laguna' 'Ričards Marcinkevičs'\n 'Julia E. Vogt']"
]
|
null | null | 2406.19280 | null | null | http://arxiv.org/pdf/2406.19280v1 | 2024-06-27T15:50:41Z | 2024-06-27T15:50:41Z | HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into
Multimodal LLMs at Scale | The rapid development of multimodal large language models (MLLMs), such as GPT-4V, has led to significant advancements. However, these models still face challenges in medical multimodal capabilities due to limitations in the quantity and quality of medical vision-text data, stemming from data privacy concerns and high annotation costs. While pioneering approaches utilize PubMed's large-scale, de-identified medical image-text pairs to address these limitations, they still fall short due to inherent data noise. To tackle this, we refined medical image-text pairs from PubMed and employed MLLMs (GPT-4V) in an 'unblinded' capacity to denoise and reformat the data, resulting in the creation of the PubMedVision dataset with 1.3 million medical VQA samples. Our validation demonstrates that: (1) PubMedVision can significantly enhance the medical multimodal capabilities of current MLLMs, showing significant improvement in benchmarks including the MMMU Health & Medicine track; (2) manual checks by medical experts and empirical results validate the superior data quality of our dataset compared to other data construction methods. Using PubMedVision, we train a 34B medical MLLM HuatuoGPT-Vision, which shows superior performance in medical multimodal scenarios among open-source MLLMs. | [
"['Junying Chen' 'Ruyi Ouyang' 'Anningzhe Gao' 'Shunian Chen'\n 'Guiming Hardy Chen' 'Xidong Wang' 'Ruifei Zhang' 'Zhenyang Cai' 'Ke Ji'\n 'Guangjun Yu' 'Xiang Wan' 'Benyou Wang']"
]
|
null | null | 2406.19292 | null | null | http://arxiv.org/pdf/2406.19292v1 | 2024-06-27T16:05:13Z | 2024-06-27T16:05:13Z | From Artificial Needles to Real Haystacks: Improving Retrieval
Capabilities in LLMs by Finetuning on Synthetic Data | Recent studies have shown that Large Language Models (LLMs) struggle to accurately retrieve information and maintain reasoning capabilities when processing long-context inputs. To address these limitations, we propose a finetuning approach utilizing a carefully designed synthetic dataset comprising numerical key-value retrieval tasks. Our experiments on models like GPT-3.5 Turbo and Mistral 7B demonstrate that finetuning LLMs on this dataset significantly improves LLMs' information retrieval and reasoning capabilities in longer-context settings. We present an analysis of the finetuned models, illustrating the transfer of skills from synthetic to real task evaluations (e.g., $10.5%$ improvement on $20$ documents MDQA at position $10$ for GPT-3.5 Turbo). We also find that finetuned LLMs' performance on general benchmarks remains almost constant while LLMs finetuned on other baseline long-context augmentation data can encourage hallucination (e.g., on TriviaQA, Mistral 7B finetuned on our synthetic data cause no performance drop while other baseline data can cause a drop that ranges from $2.33%$ to $6.19%$). Our study highlights the potential of finetuning on synthetic data for improving the performance of LLMs on longer-context tasks. | [
"['Zheyang Xiong' 'Vasilis Papageorgiou' 'Kangwook Lee'\n 'Dimitris Papailiopoulos']"
]
|
null | null | 2406.19298 | null | null | http://arxiv.org/pdf/2406.19298v1 | 2024-06-27T16:13:34Z | 2024-06-27T16:13:34Z | Compositional Image Decomposition with Diffusion Models | Given an image of a natural scene, we are able to quickly decompose it into a set of components such as objects, lighting, shadows, and foreground. We can then envision a scene where we combine certain components with those from other images, for instance a set of objects from our bedroom and animals from a zoo under the lighting conditions of a forest, even if we have never encountered such a scene before. In this paper, we present a method to decompose an image into such compositional components. Our approach, Decomp Diffusion, is an unsupervised method which, when given a single image, infers a set of different components in the image, each represented by a diffusion model. We demonstrate how components can capture different factors of the scene, ranging from global scene descriptors like shadows or facial expression to local scene descriptors like constituent objects. We further illustrate how inferred factors can be flexibly composed, even with factors inferred from other models, to generate a variety of scenes sharply different than those seen in training time. Website and code at https://energy-based-model.github.io/decomp-diffusion. | [
"['Jocelin Su' 'Nan Liu' 'Yanbo Wang' 'Joshua B. Tenenbaum' 'Yilun Du']"
]
|
null | null | 2406.19300 | null | null | http://arxiv.org/pdf/2406.19300v2 | 2024-07-09T18:17:26Z | 2024-06-27T16:16:55Z | scTree: Discovering Cellular Hierarchies in the Presence of Batch
Effects in scRNA-seq Data | We propose a novel method, scTree, for single-cell Tree Variational Autoencoders, extending a hierarchical clustering approach to single-cell RNA sequencing data. scTree corrects for batch effects while simultaneously learning a tree-structured data representation. This VAE-based method allows for a more in-depth understanding of complex cellular landscapes independently of the biasing effects of batches. We show empirically on seven datasets that scTree discovers the underlying clusters of the data and the hierarchical relations between them, as well as outperforms established baseline methods across these datasets. Additionally, we analyze the learned hierarchy to understand its biological relevance, thus underpinning the importance of integrating batch correction directly into the clustering procedure. | [
"['Moritz Vandenhirtz' 'Florian Barkmann' 'Laura Manduchi' 'Julia E. Vogt'\n 'Valentina Boeva']"
]
|
null | null | 2406.19301 | null | null | http://arxiv.org/pdf/2406.19301v1 | 2024-06-27T16:17:26Z | 2024-06-27T16:17:26Z | MCNC: Manifold Constrained Network Compression | The outstanding performance of large foundational models across diverse tasks-from computer vision to speech and natural language processing-has significantly increased their demand. However, storing and transmitting these models pose significant challenges due to their massive size (e.g., 350GB for GPT-3). Recent literature has focused on compressing the original weights or reducing the number of parameters required for fine-tuning these models. These compression methods typically involve constraining the parameter space, for example, through low-rank reparametrization (e.g., LoRA) or quantization (e.g., QLoRA) during model training. In this paper, we present MCNC as a novel model compression method that constrains the parameter space to low-dimensional pre-defined and frozen nonlinear manifolds, which effectively cover this space. Given the prevalence of good solutions in over-parameterized deep neural networks, we show that by constraining the parameter space to our proposed manifold, we can identify high-quality solutions while achieving unprecedented compression rates across a wide variety of tasks. Through extensive experiments in computer vision and natural language processing tasks, we demonstrate that our method, MCNC, significantly outperforms state-of-the-art baselines in terms of compression, accuracy, and/or model reconstruction time. | [
"['Chayne Thrash' 'Ali Abbasi' 'Parsa Nooralinejad'\n 'Soroush Abbasi Koohpayegani' 'Reed Andreas' 'Hamed Pirsiavash'\n 'Soheil Kolouri']"
]
|
null | null | 2406.19302 | null | null | http://arxiv.org/pdf/2406.19302v1 | 2024-06-27T16:17:33Z | 2024-06-27T16:17:33Z | Mapping Land Naturalness from Sentinel-2 using Deep Contextual and
Geographical Priors | In recent decades, the causes and consequences of climate change have accelerated, affecting our planet on an unprecedented scale. This change is closely tied to the ways in which humans alter their surroundings. As our actions continue to impact natural areas, using satellite images to observe and measure these effects has become crucial for understanding and combating climate change. Aiming to map land naturalness on the continuum of modern human pressure, we have developed a multi-modal supervised deep learning framework that addresses the unique challenges of satellite data and the task at hand. We incorporate contextual and geographical priors, represented by corresponding coordinate information and broader contextual information, including and surrounding the immediate patch to be predicted. Our framework improves the model's predictive performance in mapping land naturalness from Sentinel-2 data, a type of multi-spectral optical satellite imagery. Recognizing that our protective measures are only as effective as our understanding of the ecosystem, quantifying naturalness serves as a crucial step toward enhancing our environmental stewardship. | [
"['Burak Ekim' 'Michael Schmitt']"
]
|
null | null | 2406.19314 | null | null | http://arxiv.org/pdf/2406.19314v1 | 2024-06-27T16:47:42Z | 2024-06-27T16:47:42Z | LiveBench: A Challenging, Contamination-Free LLM Benchmark | Test set contamination, wherein test data from a benchmark ends up in a newer model's training set, is a well-documented obstacle for fair LLM evaluation and can quickly render benchmarks obsolete. To mitigate this, many recent benchmarks crowdsource new prompts and evaluations from human or LLM judges; however, these can introduce significant biases, and break down when scoring hard questions. In this work, we introduce a new benchmark for LLMs designed to be immune to both test set contamination and the pitfalls of LLM judging and human crowdsourcing. We release LiveBench, the first benchmark that (1) contains frequently-updated questions from recent information sources, (2) scores answers automatically according to objective ground-truth values, and (3) contains a wide variety of challenging tasks, spanning math, coding, reasoning, language, instruction following, and data analysis. To achieve this, LiveBench contains questions that are based on recently-released math competitions, arXiv papers, news articles, and datasets, and it contains harder, contamination-free versions of tasks from previous benchmarks such as Big-Bench Hard, AMPS, and IFEval. We evaluate many prominent closed-source models, as well as dozens of open-source models ranging from 0.5B to 110B in size. LiveBench is difficult, with top models achieving below 65% accuracy. We release all questions, code, and model answers. Questions will be added and updated on a monthly basis, and we will release new tasks and harder versions of tasks over time so that LiveBench can distinguish between the capabilities of LLMs as they improve in the future. We welcome community engagement and collaboration for expanding the benchmark tasks and models. | [
"['Colin White' 'Samuel Dooley' 'Manley Roberts' 'Arka Pal' 'Ben Feuer'\n 'Siddhartha Jain' 'Ravid Shwartz-Ziv' 'Neel Jain' 'Khalid Saifullah'\n 'Siddartha Naidu' 'Chinmay Hegde' 'Yann LeCun' 'Tom Goldstein'\n 'Willie Neiswanger' 'Micah Goldblum']"
]
|
null | null | 2406.19317 | null | null | http://arxiv.org/pdf/2406.19317v1 | 2024-06-27T16:52:19Z | 2024-06-27T16:52:19Z | Jump Starting Bandits with LLM-Generated Prior Knowledge | We present substantial evidence demonstrating the benefits of integrating Large Language Models (LLMs) with a Contextual Multi-Armed Bandit framework. Contextual bandits have been widely used in recommendation systems to generate personalized suggestions based on user-specific contexts. We show that LLMs, pre-trained on extensive corpora rich in human knowledge and preferences, can simulate human behaviours well enough to jump-start contextual multi-armed bandits to reduce online learning regret. We propose an initialization algorithm for contextual bandits by prompting LLMs to produce a pre-training dataset of approximate human preferences for the bandit. This significantly reduces online learning regret and data-gathering costs for training such models. Our approach is validated empirically through two sets of experiments with different bandit setups: one which utilizes LLMs to serve as an oracle and a real-world experiment utilizing data from a conjoint survey experiment. | [
"['Parand A. Alamdari' 'Yanshuai Cao' 'Kevin H. Wilson']"
]
|
null | null | 2406.19320 | null | null | http://arxiv.org/pdf/2406.19320v1 | 2024-06-27T16:54:12Z | 2024-06-27T16:54:12Z | Efficient World Models with Context-Aware Tokenization | Scaling up deep Reinforcement Learning (RL) methods presents a significant challenge. Following developments in generative modelling, model-based RL positions itself as a strong contender. Recent advances in sequence modelling have led to effective transformer-based world models, albeit at the price of heavy computations due to the long sequences of tokens required to accurately simulate environments. In this work, we propose $Delta$-IRIS, a new agent with a world model architecture composed of a discrete autoencoder that encodes stochastic deltas between time steps and an autoregressive transformer that predicts future deltas by summarizing the current state of the world with continuous tokens. In the Crafter benchmark, $Delta$-IRIS sets a new state of the art at multiple frame budgets, while being an order of magnitude faster to train than previous attention-based approaches. We release our code and models at https://github.com/vmicheli/delta-iris. | [
"['Vincent Micheli' 'Eloi Alonso' 'François Fleuret']"
]
|
null | null | 2406.19328 | null | null | http://arxiv.org/pdf/2406.19328v1 | 2024-06-27T16:59:14Z | 2024-06-27T16:59:14Z | Subtractive Training for Music Stem Insertion using Latent Diffusion
Models | We present Subtractive Training, a simple and novel method for synthesizing individual musical instrument stems given other instruments as context. This method pairs a dataset of complete music mixes with 1) a variant of the dataset lacking a specific stem, and 2) LLM-generated instructions describing how the missing stem should be reintroduced. We then fine-tune a pretrained text-to-audio diffusion model to generate the missing instrument stem, guided by both the existing stems and the text instruction. Our results demonstrate Subtractive Training's efficacy in creating authentic drum stems that seamlessly blend with the existing tracks. We also show that we can use the text instruction to control the generation of the inserted stem in terms of rhythm, dynamics, and genre, allowing us to modify the style of a single instrument in a full song while keeping the remaining instruments the same. Lastly, we extend this technique to MIDI formats, successfully generating compatible bass, drum, and guitar parts for incomplete arrangements. | [
"['Ivan Villa-Renteria' 'Mason L. Wang' 'Zachary Shah' 'Zhe Li'\n 'Soohyun Kim' 'Neelesh Ramachandran' 'Mert Pilanci']"
]
|
null | null | 2406.19356 | null | null | http://arxiv.org/pdf/2406.19356v1 | 2024-06-27T17:37:31Z | 2024-06-27T17:37:31Z | DiVERT: Distractor Generation with Variational Errors Represented as
Text for Math Multiple-choice Questions | High-quality distractors are crucial to both the assessment and pedagogical value of multiple-choice questions (MCQs), where manually crafting ones that anticipate knowledge deficiencies or misconceptions among real students is difficult. Meanwhile, automated distractor generation, even with the help of large language models (LLMs), remains challenging for subjects like math. It is crucial to not only identify plausible distractors but also understand the error behind them. In this paper, we introduce DiVERT (Distractor Generation with Variational Errors Represented as Text), a novel variational approach that learns an interpretable representation of errors behind distractors in math MCQs. Through experiments on a real-world math MCQ dataset with 1,434 questions used by hundreds of thousands of students, we show that DiVERT, despite using a base open-source LLM with 7B parameters, outperforms state-of-the-art approaches using GPT-4o on downstream distractor generation. We also conduct a human evaluation with math educators and find that DiVERT leads to error labels that are of comparable quality to human-authored ones. | [
"['Nigel Fernandez' 'Alexander Scarlatos' 'Simon Woodhead' 'Andrew Lan']"
]
|
null | null | 2406.19370 | null | null | http://arxiv.org/pdf/2406.19370v1 | 2024-06-27T17:50:05Z | 2024-06-27T17:50:05Z | Emergence of Hidden Capabilities: Exploring Learning Dynamics in Concept
Space | Modern generative models demonstrate impressive capabilities, likely stemming from an ability to identify and manipulate abstract concepts underlying their training data. However, fundamental questions remain: what determines the concepts a model learns, the order in which it learns them, and its ability to manipulate those concepts? To address these questions, we propose analyzing a model's learning dynamics via a framework we call the concept space, where each axis represents an independent concept underlying the data generating process. By characterizing learning dynamics in this space, we identify how the speed at which a concept is learned, and hence the order of concept learning, is controlled by properties of the data we term concept signal. Further, we observe moments of sudden turns in the direction of a model's learning dynamics in concept space. Surprisingly, these points precisely correspond to the emergence of hidden capabilities, i.e., where latent interventions show the model possesses the capability to manipulate a concept, but these capabilities cannot yet be elicited via naive input prompting. While our results focus on synthetically defined toy datasets, we hypothesize a general claim on emergence of hidden capabilities may hold: generative models possess latent capabilities that emerge suddenly and consistently during training, though a model might not exhibit these capabilities under naive input prompting. | [
"['Core Francisco Park' 'Maya Okawa' 'Andrew Lee' 'Ekdeep Singh Lubana'\n 'Hidenori Tanaka']"
]
|
null | null | 2406.19380 | null | null | http://arxiv.org/pdf/2406.19380v2 | 2024-07-01T23:01:33Z | 2024-06-27T17:55:31Z | TabReD: A Benchmark of Tabular Machine Learning in-the-Wild | Benchmarks that closely reflect downstream application scenarios are essential for the streamlined adoption of new research in tabular machine learning (ML). In this work, we examine existing tabular benchmarks and find two common characteristics of industry-grade tabular data that are underrepresented in the datasets available to the academic community. First, tabular data often changes over time in real-world deployment scenarios. This impacts model performance and requires time-based train and test splits for correct model evaluation. Yet, existing academic tabular datasets often lack timestamp metadata to enable such evaluation. Second, a considerable portion of datasets in production settings stem from extensive data acquisition and feature engineering pipelines. For each specific dataset, this can have a different impact on the absolute and relative number of predictive, uninformative, and correlated features, which in turn can affect model selection. To fill the aforementioned gaps in academic benchmarks, we introduce TabReD -- a collection of eight industry-grade tabular datasets covering a wide range of domains from finance to food delivery services. We assess a large number of tabular ML models in the feature-rich, temporally-evolving data setting facilitated by TabReD. We demonstrate that evaluation on time-based data splits leads to different methods ranking, compared to evaluation on random splits more common in academic benchmarks. Furthermore, on the TabReD datasets, MLP-like architectures and GBDT show the best results, while more sophisticated DL models are yet to prove their effectiveness. | [
"['Ivan Rubachev' 'Nikolay Kartashev' 'Yury Gorishniy' 'Artem Babenko']"
]
|
null | null | 2406.19384 | null | null | http://arxiv.org/pdf/2406.19384v1 | 2024-06-27T17:57:03Z | 2024-06-27T17:57:03Z | The Remarkable Robustness of LLMs: Stages of Inference? | We demonstrate and investigate the remarkable robustness of Large Language Models by deleting and swapping adjacent layers. We find that deleting and swapping interventions retain 72-95% of the original model's prediction accuracy without fine-tuning, whereas models with more layers exhibit more robustness. Based on the results of the layer-wise intervention and further experiments, we hypothesize the existence of four universal stages of inference across eight different models: detokenization, feature engineering, prediction ensembling, and residual sharpening. The first stage integrates local information, lifting raw token representations into higher-level contextual representations. Next is the iterative refinement of task and entity-specific features. Then, the second half of the model begins with a phase transition, where hidden representations align more with the vocabulary space due to specialized model components. Finally, the last layer sharpens the following token distribution by eliminating obsolete features that add noise to the prediction. | [
"['Vedang Lad' 'Wes Gurnee' 'Max Tegmark']"
]
|
null | null | 2406.19402 | null | null | http://arxiv.org/pdf/2406.19402v1 | 2024-06-01T12:03:57Z | 2024-06-01T12:03:57Z | Modelling financial volume curves with hierarchical Poisson processes | Modeling the trading volume curves of financial instruments throughout the day is of key interest in financial trading applications. Predictions of these so-called volume profiles guide trade execution strategies, for example, a common strategy is to trade a desired quantity across many orders in line with the expected volume curve throughout the day so as not to impact the price of the instrument. The volume curves (for each day) are naturally grouped by stock and can be further gathered into higher-level groupings, such as by industry. In order to model such admixtures of volume curves, we introduce a hierarchical Poisson process model for the intensity functions of admixtures of inhomogenous Poisson processes, which represent the trading times of the stock throughout the day. The model is based on the hierarchical Dirichlet process, and an efficient Markov Chain Monte Carlo (MCMC) algorithm is derived following the slice sampling framework for Bayesian nonparametric mixture models. We demonstrate the method on datasets of different stocks from the Trade and Quote repository maintained by Wharton Research Data Services, including the most liquid stock on the NASDAQ stock exchange, Apple, demonstrating the scalability of the approach. | [
"['Creighton Heaukulani' 'Abhinav Pandey' 'Lancelot F. James']"
]
|
null | null | 2406.19403 | null | null | http://arxiv.org/pdf/2406.19403v1 | 2024-06-04T15:28:06Z | 2024-06-04T15:28:06Z | Temporal distribution of clusters of investors and their application in
prediction with expert advice | Financial organisations such as brokers face a significant challenge in servicing the investment needs of thousands of their traders worldwide. This task is further compounded since individual traders will have their own risk appetite and investment goals. Traders may look to capture short-term trends in the market which last only seconds to minutes, or they may have longer-term views which last several days to months. To reduce the complexity of this task, client trades can be clustered. By examining such clusters, we would likely observe many traders following common patterns of investment, but how do these patterns vary through time? Knowledge regarding the temporal distributions of such clusters may help financial institutions manage the overall portfolio of risk that accumulates from underlying trader positions. This study contributes to the field by demonstrating that the distribution of clusters derived from the real-world trades of 20k Foreign Exchange (FX) traders (from 2015 to 2017) is described in accordance with Ewens' Sampling Distribution. Further, we show that the Aggregating Algorithm (AA), an on-line prediction with expert advice algorithm, can be applied to the aforementioned real-world data in order to improve the returns of portfolios of trader risk. However we found that the AA 'struggles' when presented with too many trader ``experts'', especially when there are many trades with similar overall patterns. To help overcome this challenge, we have applied and compared the use of Statistically Validated Networks (SVN) with a hierarchical clustering approach on a subset of the data, demonstrating that both approaches can be used to significantly improve results of the AA in terms of profitability and smoothness of returns. | [
"['Wojciech Wisniewski' 'Yuri Kalnishkan' 'David Lindsay' 'Siân Lindsay']"
]
|
null | null | 2406.19413 | null | null | http://arxiv.org/pdf/2406.19413v1 | 2024-06-18T14:07:27Z | 2024-06-18T14:07:27Z | Saliency Attention and Semantic Similarity-Driven Adversarial
Perturbation | In this paper, we introduce an enhanced textual adversarial attack method, known as Saliency Attention and Semantic Similarity driven adversarial Perturbation (SASSP). The proposed scheme is designed to improve the effectiveness of contextual perturbations by integrating saliency, attention, and semantic similarity. Traditional adversarial attack methods often struggle to maintain semantic consistency and coherence while effectively deceiving target models. Our proposed approach addresses these challenges by incorporating a three-pronged strategy for word selection and perturbation. First, we utilize a saliency-based word selection to prioritize words for modification based on their importance to the model's prediction. Second, attention mechanisms are employed to focus perturbations on contextually significant words, enhancing the attack's efficacy. Finally, an advanced semantic similarity-checking method is employed that includes embedding-based similarity and paraphrase detection. By leveraging models like Sentence-BERT for embedding similarity and fine-tuned paraphrase detection models from the Sentence Transformers library, the scheme ensures that the perturbed text remains contextually appropriate and semantically consistent with the original. Empirical evaluations demonstrate that SASSP generates adversarial examples that not only maintain high semantic fidelity but also effectively deceive state-of-the-art natural language processing models. Moreover, in comparison to the original scheme of contextual perturbation CLARE, SASSP has yielded a higher attack success rate and lower word perturbation rate. | [
"['Hetvi Waghela' 'Jaydip Sen' 'Sneha Rakshit']"
]
|
null | null | 2406.19414 | null | null | http://arxiv.org/pdf/2406.19414v1 | 2024-06-19T13:13:06Z | 2024-06-19T13:13:06Z | Stock Volume Forecasting with Advanced Information by Conditional
Variational Auto-Encoder | We demonstrate the use of Conditional Variational Encoder (CVAE) to improve the forecasts of daily stock volume time series in both short and long term forecasting tasks, with the use of advanced information of input variables such as rebalancing dates. CVAE generates non-linear time series as out-of-sample forecasts, which have better accuracy and closer fit of correlation to the actual data, compared to traditional linear models. These generative forecasts can also be used for scenario generation, which aids interpretation. We further discuss correlations in non-stationary time series and other potential extensions from the CVAE forecasts. | [
"['Parley R Yang' 'Alexander Y Shestopaloff']"
]
|
null | null | 2406.19475 | null | null | http://arxiv.org/pdf/2406.19475v1 | 2024-06-27T18:38:42Z | 2024-06-27T18:38:42Z | Stochastic First-Order Methods with Non-smooth and Non-Euclidean
Proximal Terms for Nonconvex High-Dimensional Stochastic Optimization | When the nonconvex problem is complicated by stochasticity, the sample complexity of stochastic first-order methods may depend linearly on the problem dimension, which is undesirable for large-scale problems. In this work, we propose dimension-insensitive stochastic first-order methods (DISFOMs) to address nonconvex optimization with expected-valued objective function. Our algorithms allow for non-Euclidean and non-smooth distance functions as the proximal terms. Under mild assumptions, we show that DISFOM using minibatches to estimate the gradient enjoys sample complexity of $ mathcal{O} ( (log d) / epsilon^4 ) $ to obtain an $epsilon$-stationary point. Furthermore, we prove that DISFOM employing variance reduction can sharpen this bound to $mathcal{O} ( (log d)^{2/3}/epsilon^{10/3} )$, which perhaps leads to the best-known sample complexity result in terms of $d$. We provide two choices of the non-smooth distance functions, both of which allow for closed-form solutions to the proximal step. Numerical experiments are conducted to illustrate the dimension insensitive property of the proposed frameworks. | [
"['Yue Xie' 'Jiawen Bi' 'Hongcheng Liu']"
]
|
null | null | 2406.19477 | null | null | http://arxiv.org/abs/2406.19477v1 | 2024-06-27T18:40:55Z | 2024-06-27T18:40:55Z | Multi-agent Cooperative Games Using Belief Map Assisted Training | In a multi-agent system, agents share their local observations to gain global situational awareness for decision making and collaboration using a message passing system. When to send a message, how to encode a message, and how to leverage the received messages directly affect the effectiveness of the collaboration among agents. When training a multi-agent cooperative game using reinforcement learning (RL), the message passing system needs to be optimized together with the agent policies. This consequently increases the model's complexity and poses significant challenges to the convergence and performance of learning. To address this issue, we propose the Belief-map Assisted Multi-agent System (BAMS), which leverages a neuro-symbolic belief map to enhance training. The belief map decodes the agent's hidden state to provide a symbolic representation of the agent's understanding of the environment and other agent's status. The simplicity of symbolic representation allows the gathering and comparison of the ground truth information with the belief, which provides an additional channel of feedback for the learning. Compared to the sporadic and delayed feedback coming from the reward in RL, the feedback from the belief map is more consistent and reliable. Agents using BAMS can learn a more effective message passing network to better understand each other, resulting in better performance in a cooperative predator and prey game with varying levels of map complexity and compare it to previous multi-agent message passing models. The simulation results showed that BAMS reduced training epochs by 66%, and agents who apply the BAMS model completed the game with 34.62% fewer steps on average. | [
"['Qinwei Huang' 'Chen Luo' 'Alex B. Wu' 'Simon Khan' 'Hai Li' 'Qinru Qiu']"
]
|
null | null | 2406.19486 | null | null | http://arxiv.org/pdf/2406.19486v1 | 2024-06-27T19:02:41Z | 2024-06-27T19:02:41Z | LoPT: Low-Rank Prompt Tuning for Parameter Efficient Language Models | In prompt tuning, a prefix or suffix text is added to the prompt, and the embeddings (soft prompts) or token indices (hard prompts) of the prefix/suffix are optimized to gain more control over language models for specific tasks. This approach eliminates the need for hand-crafted prompt engineering or explicit model fine-tuning. Prompt tuning is significantly more parameter-efficient than model fine-tuning, as it involves optimizing partial inputs of language models to produce desired outputs. In this work, we aim to further reduce the amount of trainable parameters required for a language model to perform well on specific tasks. We propose Low-rank Prompt Tuning (LoPT), a low-rank model for prompts that achieves efficient prompt optimization. The proposed method demonstrates similar outcomes to full parameter prompt tuning while reducing the number of trainable parameters by a factor of 5. It also provides promising results compared to the state-of-the-art methods that would require 10 to 20 times more parameters. | [
"['Shouchang Guo' 'Sonam Damani' 'Keng-hao Chang']"
]
|
null | null | 2406.19501 | null | null | http://arxiv.org/pdf/2406.19501v1 | 2024-06-27T19:28:43Z | 2024-06-27T19:28:43Z | Monitoring Latent World States in Language Models with Propositional
Probes | Language models are susceptible to bias, sycophancy, backdoors, and other tendencies that lead to unfaithful responses to the input context. Interpreting internal states of language models could help monitor and correct unfaithful behavior. We hypothesize that language models represent their input contexts in a latent world model, and seek to extract this latent world state from the activations. We do so with 'propositional probes', which compositionally probe tokens for lexical information and bind them into logical propositions representing the world state. For example, given the input context ''Greg is a nurse. Laura is a physicist.'', we decode the propositions ''WorksAs(Greg, nurse)'' and ''WorksAs(Laura, physicist)'' from the model's activations. Key to this is identifying a 'binding subspace' in which bound tokens have high similarity (''Greg'' and ''nurse'') but unbound ones do not (''Greg'' and ''physicist''). We validate propositional probes in a closed-world setting with finitely many predicates and properties. Despite being trained on simple templated contexts, propositional probes generalize to contexts rewritten as short stories and translated to Spanish. Moreover, we find that in three settings where language models respond unfaithfully to the input context -- prompt injections, backdoor attacks, and gender bias -- the decoded propositions remain faithful. This suggests that language models often encode a faithful world model but decode it unfaithfully, which motivates the search for better interpretability tools for monitoring LMs. | [
"['Jiahai Feng' 'Stuart Russell' 'Jacob Steinhardt']"
]
|
null | null | 2406.19507 | null | null | http://arxiv.org/pdf/2406.19507v1 | 2024-06-27T19:58:11Z | 2024-06-27T19:58:11Z | Too Good to be True? Turn Any Model Differentially Private With
DP-Weights | Imagine training a machine learning model with Differentially Private Stochastic Gradient Descent (DP-SGD), only to discover post-training that the noise level was either too high, crippling your model's utility, or too low, compromising privacy. The dreaded realization hits: you must start the lengthy training process from scratch. But what if you could avoid this retraining nightmare? In this study, we introduce a groundbreaking approach (to our knowledge) that applies differential privacy noise to the model's weights after training. We offer a comprehensive mathematical proof for this novel approach's privacy bounds, use formal methods to validate its privacy guarantees, and empirically evaluate its effectiveness using membership inference attacks and performance evaluations. This method allows for a single training run, followed by post-hoc noise adjustments to achieve optimal privacy-utility trade-offs. We compare this novel fine-tuned model (DP-Weights model) to a traditional DP-SGD model, demonstrating that our approach yields statistically similar performance and privacy guarantees. Our results validate the efficacy of post-training noise application, promising significant time savings and flexibility in fine-tuning differential privacy parameters, making it a practical alternative for deploying differentially private models in real-world scenarios. | [
"['David Zagardo']"
]
|
null | null | 2406.19522 | null | null | http://arxiv.org/pdf/2406.19522v1 | 2024-06-27T20:45:08Z | 2024-06-27T20:45:08Z | Reliable edge machine learning hardware for scientific applications | Extreme data rate scientific experiments create massive amounts of data that require efficient ML edge processing. This leads to unique validation challenges for VLSI implementations of ML algorithms: enabling bit-accurate functional simulations for performance validation in experimental software frameworks, verifying those ML models are robust under extreme quantization and pruning, and enabling ultra-fine-grained model inspection for efficient fault tolerance. We discuss approaches to developing and validating reliable algorithms at the scientific edge under such strict latency, resource, power, and area requirements in extreme experimental environments. We study metrics for developing robust algorithms, present preliminary results and mitigation strategies, and conclude with an outlook of these and future directions of research towards the longer-term goal of developing autonomous scientific experimentation methods for accelerated scientific discovery. | [
"['Tommaso Baldi' 'Javier Campos' 'Ben Hawks' 'Jennifer Ngadiuba'\n 'Nhan Tran' 'Daniel Diaz' 'Javier Duarte' 'Ryan Kastner' 'Andres Meza'\n 'Melissa Quinnan' 'Olivia Weng' 'Caleb Geniesse' 'Amir Gholami'\n 'Michael W. Mahoney' 'Vladimir Loncar' 'Philip Harris' 'Joshua Agar'\n 'Shuyu Qin']"
]
|
null | null | 2406.19524 | null | null | http://arxiv.org/pdf/2406.19524v1 | 2024-06-27T20:50:06Z | 2024-06-27T20:50:06Z | Bayesian calibration of stochastic agent based model via random forest | Agent-based models (ABM) provide an excellent framework for modeling outbreaks and interventions in epidemiology by explicitly accounting for diverse individual interactions and environments. However, these models are usually stochastic and highly parametrized, requiring precise calibration for predictive performance. When considering realistic numbers of agents and properly accounting for stochasticity, this high dimensional calibration can be computationally prohibitive. This paper presents a random forest based surrogate modeling technique to accelerate the evaluation of ABMs and demonstrates its use to calibrate an epidemiological ABM named CityCOVID via Markov chain Monte Carlo (MCMC). The technique is first outlined in the context of CityCOVID's quantities of interest, namely hospitalizations and deaths, by exploring dimensionality reduction via temporal decomposition with principal component analysis (PCA) and via sensitivity analysis. The calibration problem is then presented and samples are generated to best match COVID-19 hospitalization and death numbers in Chicago from March to June in 2020. These results are compared with previous approximate Bayesian calibration (IMABC) results and their predictive performance is analyzed showing improved performance with a reduction in computation. | [
"['Connor Robertson' 'Cosmin Safta' 'Nicholson Collier' 'Jonathan Ozik'\n 'Jaideep Ray']"
]
|
null | null | 2406.19526 | null | null | http://arxiv.org/pdf/2406.19526v1 | 2024-06-27T20:56:57Z | 2024-06-27T20:56:57Z | TocBERT: Medical Document Structure Extraction Using Bidirectional
Transformers | Text segmentation holds paramount importance in the field of Natural Language Processing (NLP). It plays an important role in several NLP downstream tasks like information retrieval and document summarization. In this work, we propose a new solution, namely TocBERT, for segmenting texts using bidirectional transformers. TocBERT represents a supervised solution trained on the detection of titles and sub-titles from their semantic representations. This task was formulated as a named entity recognition (NER) problem. The solution has been applied on a medical text segmentation use-case where the Bio-ClinicalBERT model is fine-tuned to segment discharge summaries of the MIMIC-III dataset. The performance of TocBERT has been evaluated on a human-labeled ground truth corpus of 250 notes. It achieved an F1-score of 84.6% when evaluated on a linear text segmentation problem and 72.8% on a hierarchical text segmentation problem. It outperformed a carefully designed rule-based solution, particularly in distinguishing titles from subtitles. | [
"['Majd Saleh' 'Sarra Baghdadi' 'Stéphane Paquelet']"
]
|
null | null | 2406.19531 | null | null | http://arxiv.org/pdf/2406.19531v1 | 2024-06-27T21:12:26Z | 2024-06-27T21:12:26Z | Forward and Backward State Abstractions for Off-policy Evaluation | Off-policy evaluation (OPE) is crucial for evaluating a target policy's impact offline before its deployment. However, achieving accurate OPE in large state spaces remains challenging.This paper studies state abstractions-originally designed for policy learning-in the context of OPE. Our contributions are three-fold: (i) We define a set of irrelevance conditions central to learning state abstractions for OPE. (ii) We derive sufficient conditions for achieving irrelevance in Q-functions and marginalized importance sampling ratios, the latter obtained by constructing a time-reversed Markov decision process (MDP) based on the observed MDP. (iii) We propose a novel two-step procedure that sequentially projects the original state space into a smaller space, which substantially simplify the sample complexity of OPE arising from high cardinality. | [
"['Meiling Hao' 'Pingfan Su' 'Liyuan Hu' 'Zoltan Szabo' 'Qingyuan Zhao'\n 'Chengchun Shi']"
]
|
null | null | 2406.19532 | null | null | http://arxiv.org/pdf/2406.19532v1 | 2024-06-27T21:12:48Z | 2024-06-27T21:12:48Z | Dataless Quadratic Neural Networks for the Maximum Independent Set
Problem | Combinatorial Optimization (CO) plays a crucial role in addressing various significant problems, among them the challenging Maximum Independent Set (MIS) problem. In light of recent advancements in deep learning methods, efforts have been directed towards leveraging data-driven learning approaches, typically rooted in supervised learning and reinforcement learning, to tackle the NP-hard MIS problem. However, these approaches rely on labeled datasets, exhibit weak generalization, and often depend on problem-specific heuristics. Recently, ReLU-based dataless neural networks were introduced to address combinatorial optimization problems. This paper introduces a novel dataless quadratic neural network formulation, featuring a continuous quadratic relaxation for the MIS problem. Notably, our method eliminates the need for training data by treating the given MIS instance as a trainable entity. More specifically, the graph structure and constraints of the MIS instance are used to define the structure and parameters of the neural network such that training it on a fixed input provides a solution to the problem, thereby setting it apart from traditional supervised or reinforcement learning approaches. By employing a gradient-based optimization algorithm like ADAM and leveraging an efficient off-the-shelf GPU parallel implementation, our straightforward yet effective approach demonstrates competitive or superior performance compared to state-of-the-art learning-based methods. Another significant advantage of our approach is that, unlike exact and heuristic solvers, the running time of our method scales only with the number of nodes in the graph, not the number of edges. | [
"['Ismail Alkhouri' 'Cedric Le Denmat' 'Yingjie Li' 'Cunxi Yu' 'Jia Liu'\n 'Rongrong Wang' 'Alvaro Velasquez']"
]
|
null | null | 2406.19549 | null | null | http://arxiv.org/pdf/2406.19549v2 | 2024-07-01T04:52:56Z | 2024-06-27T22:01:00Z | ASCENT: Amplifying Power Side-Channel Resilience via Learning &
Monte-Carlo Tree Search | Power side-channel (PSC) analysis is pivotal for securing cryptographic hardware. Prior art focused on securing gate-level netlists obtained as-is from chip design automation, neglecting all the complexities and potential side-effects for security arising from the design automation process. That is, automation traditionally prioritizes power, performance, and area (PPA), sidelining security. We propose a "security-first" approach, refining the logic synthesis stage to enhance the overall resilience of PSC countermeasures. We introduce ASCENT, a learning-and-search-based framework that (i) drastically reduces the time for post-design PSC evaluation and (ii) explores the security-vs-PPA design space. Thus, ASCENT enables an efficient exploration of a large number of candidate netlists, leading to an improvement in PSC resilience compared to regular PPA-optimized netlists. ASCENT is up to 120x faster than traditional PSC analysis and yields a 3.11x improvement for PSC resilience of state-of-the-art PSC countermeasures | [
"['Jitendra Bhandari' 'Animesh Basak Chowdhury' 'Mohammed Nabeel'\n 'Ozgur Sinanoglu' 'Siddharth Garg' 'Ramesh Karri' 'Johann Knechtel']"
]
|
null | null | 2406.19552 | null | null | http://arxiv.org/pdf/2406.19552v1 | 2024-06-27T22:08:22Z | 2024-06-27T22:08:22Z | Rethinking harmless refusals when fine-tuning foundation models | In this paper, we investigate the degree to which fine-tuning in Large Language Models (LLMs) effectively mitigates versus merely conceals undesirable behavior. Through the lens of semi-realistic role-playing exercises designed to elicit such behaviors, we explore the response dynamics of LLMs post fine-tuning interventions. Our methodology involves prompting models for Chain-of-Thought (CoT) reasoning and analyzing the coherence between the reasoning traces and the resultant outputs. Notably, we identify a pervasive phenomenon we term emph{reason-based deception}, where models either stop producing reasoning traces or produce seemingly ethical reasoning traces that belie the unethical nature of their final outputs. We further examine the efficacy of response strategies (polite refusal versus explicit rebuttal) in curbing the occurrence of undesired behavior in subsequent outputs of multi-turn interactions. Our findings reveal that explicit rebuttals significantly outperform polite refusals in preventing the continuation of undesired outputs and nearly eliminate reason-based deception, challenging current practices in model fine-tuning. Accordingly, the two key contributions of this paper are (1) defining and studying reason-based deception, a new type of hidden behavior, and (2) demonstrating that rebuttals provide a more robust response model to harmful requests than refusals, thereby highlighting the need to reconsider the response strategies in fine-tuning approaches. | [
"['Florin Pop' 'Judd Rosenblatt' 'Diogo Schwerz de Lucena' 'Michael Vaiana']"
]
|
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