pwc-archive/papers-with-abstracts huggingface dataset:
Daily Physical Activity Monitoring -- Adaptive Learning from Multi-source Motion Sensor Data
Authors: Haoting Zhang, Donglin Zhan, Yunduan Lin, Jinghai He, Qing Zhu, Zuo-Jun Max Shen, Zeyu Zheng
Date: 2024-05-26 00:00:00
Tasks: Transfer Learning

In healthcare applications, there is a growing need to develop machine learning models that use data from a single source, such as that from a wrist wearable device, to monitor physical activities, assess health risks, and provide immediate health recommendations or interventions. However, the limitation of using single-source data often compromises the model's accuracy, as it fails to capture the full scope of human activities. While a more comprehensive dataset can be gathered in a lab setting using multiple sensors attached to various body parts, this approach is not practical for everyday use due to the impracticality of wearing multiple sensors. To address this challenge, we introduce a transfer learning framework that optimizes machine learning models for everyday applications by leveraging multi-source data collected in a laboratory setting. We introduce a novel metric to leverage the inherent relationship between these multiple data sources, as they are all paired to capture aspects of the same physical activity. Through numerical experiments, our framework outperforms existing methods in classification accuracy and robustness to noise, offering a promising avenue for the enhancement of daily activity monitoring.
When does compositional structure yield compositional generalization? A kernel theory
Authors: Samuel Lippl, Kim Stachenfeld
Date: 2024-05-26 00:00:00
Tasks: Memorization, Representation Learning

Compositional generalization (the ability to respond correctly to novel combinations of familiar components) is thought to be a cornerstone of intelligent behavior. Compositionally structured (e.g. disentangled) representations support this ability; however, the conditions under which they are sufficient for the emergence of compositional generalization remain unclear. To address this gap, we present a theory of compositional generalization in kernel models with fixed, compositionally structured representations. This provides a tractable framework for characterizing the impact of training data statistics on generalization. We find that these models are limited to functions that assign values to each combination of components seen during training, and then sum up these values ("conjunction-wise additivity"). This imposes fundamental restrictions on the set of tasks compositionally structured kernel models can learn, in particular preventing them from transitively generalizing equivalence relations. Even for compositional tasks that they can learn in principle, we identify novel failure modes in compositional generalization (memorization leak and shortcut bias) that arise from biases in the training data. Finally, we empirically validate our theory, showing that it captures the behavior of deep neural networks (convolutional networks, residual networks, and Vision Transformers) trained on a set of compositional tasks with similarly structured data. Ultimately, this work examines how statistical structure in the training data can affect compositional generalization, with implications for how to identify and remedy failure modes in deep learning models.
Multi-Reference Preference Optimization for Large Language Models
Authors: Hung Le, Quan Tran, Dung Nguyen, Kien Do, Saloni Mittal, Kelechi Ogueji, Svetha Venkatesh
Date: 2024-05-26 00:00:00
Tasks: GSM8K, TruthfulQA

How can Large Language Models (LLMs) be aligned with human intentions and values? A typical solution is to gather human preference on model outputs and finetune the LLMs accordingly while ensuring that updates do not deviate too far from a reference model. Recent approaches, such as direct preference optimization (DPO), have eliminated the need for unstable and sluggish reinforcement learning optimization by introducing close-formed supervised losses. However, a significant limitation of the current approach is its design for a single reference model only, neglecting to leverage the collective power of numerous pretrained LLMs. To overcome this limitation, we introduce a novel closed-form formulation for direct preference optimization using multiple reference models. The resulting algorithm, Multi-Reference Preference Optimization (MRPO), leverages broader prior knowledge from diverse reference models, substantially enhancing preference learning capabilities compared to the single-reference DPO. Our experiments demonstrate that LLMs finetuned with MRPO generalize better in various preference data, regardless of data scarcity or abundance. Furthermore, MRPO effectively finetunes LLMs to exhibit superior performance in several downstream natural language processing tasks such as GSM8K and TruthfulQA.
Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference
Authors: Xunpeng Huang, Difan Zou, Hanze Dong, Yi Zhang, Yi-An Ma, Tong Zhang
Date: 2024-05-26 00:00:00
Tasks: Denoising

To generate data from trained diffusion models, most inference algorithms, such as DDPM, DDIM, and other variants, rely on discretizing the reverse SDEs or their equivalent ODEs. In this paper, we view such approaches as decomposing the entire denoising diffusion process into several segments, each corresponding to a reverse transition kernel (RTK) sampling subproblem. Specifically, DDPM uses a Gaussian approximation for the RTK, resulting in low per-subproblem complexity but requiring a large number of segments (i.e., subproblems), which is conjectured to be inefficient. To address this, we develop a general RTK framework that enables a more balanced subproblem decomposition, resulting in $\tilde O(1)$ subproblems, each with strongly log-concave targets. We then propose leveraging two fast sampling algorithms, the Metropolis-Adjusted Langevin Algorithm (MALA) and Underdamped Langevin Dynamics (ULD), for solving these strongly log-concave subproblems. This gives rise to the RTK-MALA and RTK-ULD algorithms for diffusion inference. In theory, we further develop the convergence guarantees for RTK-MALA and RTK-ULD in total variation (TV) distance: RTK-ULD can achieve $\epsilon$ target error within $\tilde{\mathcal O}(d^{1/2}\epsilon^{-1})$ under mild conditions, and RTK-MALA enjoys a $\mathcal{O}(d^{2}\log(d/\epsilon))$ convergence rate under slightly stricter conditions. These theoretical results surpass the state-of-the-art convergence rates for diffusion inference and are well supported by numerical experiments.
Rewarded Region Replay (R3) for Policy Learning with Discrete Action Space
Authors: Bangzheng Li, Ningshan Ma, Zifan Wang
Date: 2024-05-26 00:00:00

We introduce a new on-policy algorithm called Rewarded Region Replay (R3), which significantly improves on PPO in solving environments with discrete action spaces. R3 improves sample efficiency by using a replay buffer which contains past successful trajectories with reward above a certain threshold, which are used to update a PPO agent with importance sampling. Crucially, we discard the importance sampling factors which are above a certain ratio to reduce variance and stabilize training. We found that R3 significantly outperforms PPO in Minigrid environments with sparse rewards and discrete action space, such as DoorKeyEnv and CrossingEnv, and moreover we found that the improvement margin of our method versus baseline PPO increases with the complexity of the environment. We also benchmarked the performance of R3 against DDQN (Double Deep Q-Network), which is a standard baseline in off-policy methods for discrete actions, and found that R3 also outperforms DDQN agent in DoorKeyEnv. Lastly, we adapt the idea of R3 to dense reward setting to obtain the Dense R3 algorithm (or DR3) and benchmarked it against PPO on Cartpole-V1 environment. We found that DR3 outperforms PPO significantly on this dense reward environment. Our code can be found at https://github.com/chry-santhemum/R3.
Dynamic Inhomogeneous Quantum Resource Scheduling with Reinforcement Learning
Authors: Linsen Li, Pratyush Anand, Kaiming He, Dirk Englund
Date: 2024-05-25 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Scheduling

A central challenge in quantum information science and technology is achieving real-time estimation and feedforward control of quantum systems. This challenge is compounded by the inherent inhomogeneity of quantum resources, such as qubit properties and controls, and their intrinsically probabilistic nature. This leads to stochastic challenges in error detection and probabilistic outcomes in processes such as heralded remote entanglement. Given these complexities, optimizing the construction of quantum resource states is an NP-hard problem. In this paper, we address the quantum resource scheduling issue by formulating the problem and simulating it within a digitized environment, allowing the exploration and development of agent-based optimization strategies. We employ reinforcement learning agents within this probabilistic setting and introduce a new framework utilizing a Transformer model that emphasizes self-attention mechanisms for pairs of qubits. This approach facilitates dynamic scheduling by providing real-time, next-step guidance. Our method significantly improves the performance of quantum systems, achieving more than a 3$\times$ improvement over rule-based agents, and establishes an innovative framework that improves the joint design of physical and control systems for quantum applications in communication, networking, and computing.
Qsco: A Quantum Scoring Module for Open-set Supervised Anomaly Detection
Authors: Yifeng Peng, Xinyi Li, Zhiding Liang, Ying Wang
Date: 2024-05-25 00:00:00
Tasks: Anomaly Detection, Supervised Anomaly Detection

Open set anomaly detection (OSAD) is a crucial task that aims to identify abnormal patterns or behaviors in data sets, especially when the anomalies observed during training do not represent all possible classes of anomalies. The recent advances in quantum computing in handling complex data structures and improving machine learning models herald a paradigm shift in anomaly detection methodologies. This study proposes a Quantum Scoring Module (Qsco), embedding quantum variational circuits into neural networks to enhance the model's processing capabilities in handling uncertainty and unlabeled data. Extensive experiments conducted across eight real-world anomaly detection datasets demonstrate our model's superior performance in detecting anomalies across varied settings and reveal that integrating quantum simulators does not result in prohibitive time complexities. Our study validates the feasibility of quantum-enhanced anomaly detection methods in practical applications.
Noisy Data Meets Privacy: Training Local Models with Post-Processed Remote Queries
Authors: Kexin Li, Aastha Mehta, David Lie
Date: 2024-05-25 00:00:00
Tasks: Knowledge Distillation, Model extraction, Privacy Preserving, Transfer Learning

The adoption of large cloud-based models for inference in privacy-sensitive domains, such as homeless care systems and medical imaging, raises concerns about end-user data privacy. A common solution is adding locally differentially private (LDP) noise to queries before transmission, but this often reduces utility. LDPKiT, which stands for Local Differentially-Private and Utility-Preserving Inference via Knowledge Transfer, addresses the concern by generating a privacy-preserving inference dataset aligned with the private data distribution. This dataset is used to train a reliable local model for inference on sensitive inputs. LDPKiT employs a two-layer noise injection framework that leverages LDP and its post-processing property to create a privacy-protected inference dataset. The first layer ensures privacy, while the second layer helps to recover utility by creating a sufficiently large dataset for subsequent local model extraction using noisy labels returned from a cloud model on privacy-protected noisy inputs. Our experiments on Fashion-MNIST, SVHN and PathMNIST medical datasets demonstrate that LDPKiT effectively improves utility while preserving privacy. Moreover, the benefits of using LDPKiT increase at higher, more privacy-protective noise levels. For instance, on SVHN, LDPKiT achieves similar inference accuracy with $\epsilon=1.25$ as it does with $\epsilon=2.0$, providing stronger privacy guarantees with less than a 2% drop in accuracy. Furthermore, we perform extensive sensitivity analyses to evaluate the impact of dataset sizes on LDPKiT's effectiveness and systematically analyze the latent space representations to offer a theoretical explanation for its accuracy improvements. Lastly, we qualitatively and quantitatively demonstrate that the type of knowledge distillation performed by LDPKiT is ethical and fundamentally distinct from adversarial model extraction attacks.
A Differential Equation Approach for Wasserstein GANs and Beyond
Authors: Zachariah Malik, Yu-Jui Huang
Date: 2024-05-25 00:00:00

We propose a new theoretical lens to view Wasserstein generative adversarial networks (WGANs). In our framework, we define a discretization inspired by a distribution-dependent ordinary differential equation (ODE). We show that such a discretization is convergent and propose a viable class of adversarial training methods to implement this discretization, which we call W1 Forward Euler (W1-FE). In particular, the ODE framework allows us to implement persistent training, a novel training technique that cannot be applied to typical WGAN algorithms without the ODE interpretation. Remarkably, when we do not implement persistent training, we prove that our algorithms simplify to existing WGAN algorithms; when we increase the level of persistent training appropriately, our algorithms outperform existing WGAN algorithms in both low- and high-dimensional examples.
BOLD: Boolean Logic Deep Learning
Authors: Van Minh Nguyen, Cristian Ocampo, Aymen Askri, Louis Leconte, Ba-Hien Tran
Date: 2024-05-25 00:00:00
Tasks: Benchmarking, Deep Learning, Image Super-Resolution, Natural Language Understanding, Semantic Segmentation, Super-Resolution

Deep learning is computationally intensive, with significant efforts focused on reducing arithmetic complexity, particularly regarding energy consumption dominated by data movement. While existing literature emphasizes inference, training is considerably more resource-intensive. This paper proposes a novel mathematical principle by introducing the notion of Boolean variation such that neurons made of Boolean weights and inputs can be trained -- for the first time -- efficiently in Boolean domain using Boolean logic instead of gradient descent and real arithmetic. We explore its convergence, conduct extensively experimental benchmarking, and provide consistent complexity evaluation by considering chip architecture, memory hierarchy, dataflow, and arithmetic precision. Our approach achieves baseline full-precision accuracy in ImageNet classification and surpasses state-of-the-art results in semantic segmentation, with notable performance in image super-resolution, and natural language understanding with transformer-based models. Moreover, it significantly reduces energy consumption during both training and inference.
RoboArm-NMP: a Learning Environment for Neural Motion Planning
Authors: Tom Jurgenson, Matan Sudry, Gal Avineri, Aviv Tamar
Date: 2024-05-25 00:00:00
Tasks: Motion Planning, Representation Learning

We present RoboArm-NMP, a learning and evaluation environment that allows simple and thorough evaluations of Neural Motion Planning (NMP) algorithms, focused on robotic manipulators. Our Python-based environment provides baseline implementations for learning control policies (either supervised or reinforcement learning based), a simulator based on PyBullet, data of solved instances using a classical motion planning solver, various representation learning methods for encoding the obstacles, and a clean interface between the learning and planning frameworks. Using RoboArm-NMP, we compare several prominent NMP design points, and demonstrate that the best methods mostly succeed in generalizing to unseen goals in a scene with fixed obstacles, but have difficulty in generalizing to unseen obstacle configurations, suggesting focus points for future research.
Efficiently Parameterized Neural Metriplectic Systems
Authors: Anthony Gruber, Kookjin Lee, Haksoo Lim, Noseong Park, Nathaniel Trask
Date: 2024-05-25 00:00:00

Metriplectic systems are learned from data in a way that scales quadratically in both the size of the state and the rank of the metriplectic data. Besides being provably energy conserving and entropy stable, the proposed approach comes with approximation results demonstrating its ability to accurately learn metriplectic dynamics from data as well as an error estimate indicating its potential for generalization to unseen timescales when approximation error is low. Examples are provided which illustrate performance in the presence of both full state information as well as when entropic variables are unknown, confirming that the proposed approach exhibits superior accuracy and scalability without compromising on model expressivity.
LUCIE: A Lightweight Uncoupled ClImate Emulator with long-term stability and physical consistency for O(1000)-member ensembles
Authors: Haiwen Guan, Troy Arcomano, Ashesh Chattopadhyay, Romit Maulik
Date: 2024-05-25 00:00:00
Tasks: GPU

We present a lightweight, easy-to-train, low-resolution, fully data-driven climate emulator, LUCIE, that can be trained on as low as $2$ years of $6$-hourly ERA5 data. Unlike most state-of-the-art AI weather models, LUCIE remains stable and physically consistent for $100$ years of autoregressive simulation with $100$ ensemble members. Long-term mean climatology from LUCIE's simulation of temperature, wind, precipitation, and humidity matches that of ERA5 data, along with the variability. We further demonstrate how well extreme weather events and their return periods can be estimated from a large ensemble of long-term simulations. We further discuss an improved training strategy with a hard-constrained first-order integrator to suppress autoregressive error growth, a novel spectral regularization strategy to better capture fine-scale dynamics, and finally an optimization algorithm that enables data-limited (as low as $2$ years of $6$-hourly data) training of the emulator without losing stability and physical consistency. Finally, we provide a scaling experiment to compare the long-term bias of LUCIE with respect to the number of training samples. Importantly, LUCIE is an easy to use model that can be trained in just $2.4$h on a single A-100 GPU, allowing for multiple experiments that can explore important scientific questions that could be answered with large ensembles of long-term simulations, e.g., the impact of different variables on the simulation, dynamic response to external forcing, and estimation of extreme weather events, amongst others.
Comparative Analysis of Open-Source Language Models in Summarizing Medical Text Data
Authors: Yuhao Chen, Zhimu Wang, Bo Wen, Farhana Zulkernine
Date: 2024-05-25 00:00:00
Tasks: Question Answering

Unstructured text in medical notes and dialogues contains rich information. Recent advancements in Large Language Models (LLMs) have demonstrated superior performance in question answering and summarization tasks on unstructured text data, outperforming traditional text analysis approaches. However, there is a lack of scientific studies in the literature that methodically evaluate and report on the performance of different LLMs, specifically for domain-specific data such as medical chart notes. We propose an evaluation approach to analyze the performance of open-source LLMs such as Llama2 and Mistral for medical summarization tasks, using GPT-4 as an assessor. Our innovative approach to quantitative evaluation of LLMs can enable quality control, support the selection of effective LLMs for specific tasks, and advance knowledge discovery in digital health.
Generating configurations of increasing lattice size with machine learning and the inverse renormalization group
Authors: Dimitrios Bachtis
Date: 2024-05-25 00:00:00

We review recent developments of machine learning algorithms pertinent to the inverse renormalization group, which was originally established as a generative numerical method by Ron-Swendsen-Brandt via the implementation of compatible Monte Carlo simulations. Inverse renormalization group methods enable the iterative generation of configurations for increasing lattice size without the critical slowing down effect. We discuss the construction of inverse renormalization group transformations with the use of convolutional neural networks and present applications in models of statistical mechanics, lattice field theory, and disordered systems. We highlight the case of the three-dimensional Edwards-Anderson spin glass, where the inverse renormalization group can be employed to construct configurations for lattice volumes that have not yet been accessed by dedicated supercomputers.
LoGAH: Predicting 774-Million-Parameter Transformers using Graph HyperNetworks with 1/100 Parameters
Authors: Xinyu Zhou, Boris Knyazev, Alexia Jolicoeur-Martineau, Jie Fu
Date: 2024-05-25 00:00:00
Tasks: Decoder, Transfer Learning

A good initialization of deep learning models is essential since it can help them converge better and faster. However, pretraining large models is unaffordable for many researchers, which makes a desired prediction for initial parameters more necessary nowadays. Graph HyperNetworks (GHNs), one approach to predicting model parameters, have recently shown strong performance in initializing large vision models. Unfortunately, predicting parameters of very wide networks relies on copying small chunks of parameters multiple times and requires an extremely large number of parameters to support full prediction, which greatly hinders its adoption in practice. To address this limitation, we propose LoGAH (Low-rank GrAph Hypernetworks), a GHN with a low-rank parameter decoder that expands to significantly wider networks without requiring as excessive increase of parameters as in previous attempts. LoGAH allows us to predict the parameters of 774-million large neural networks in a memory-efficient manner. We show that vision and language models (i.e., ViT and GPT-2) initialized with LoGAH achieve better performance than those initialized randomly or using existing hypernetworks. Furthermore, we show promising transfer learning results w.r.t. training LoGAH on small datasets and using the predicted parameters to initialize for larger tasks. We provide the codes in https://github.com/Blackzxy/LoGAH .