pwc-archive/papers-with-abstracts huggingface dataset:
Interpreting Inflammation Prediction Model via Tag-based Cohort Explanation
Authors: Fanyu Meng, Jules Larke, Xin Liu, Zhaodan Kong, Xin Chen, Danielle Lemay, Ilias Tagkopoulos
Date: 2024-10-17 00:00:00
Tasks: Decision Making, Feature Importance, Nutrition, TAG

Machine learning is revolutionizing nutrition science by enabling systems to learn from data and make intelligent decisions. However, the complexity of these models often leads to challenges in understanding their decision-making processes, necessitating the development of explainability techniques to foster trust and increase model transparency. An under-explored type of explanation is cohort explanation, which provides explanations to groups of instances with similar characteristics. Unlike traditional methods that focus on individual explanations or global model behavior, cohort explainability bridges the gap by providing unique insights at an intermediate granularity. We propose a novel framework for identifying cohorts within a dataset based on local feature importance scores, aiming to generate concise descriptions of the clusters via tags. We evaluate our framework on a food-based inflammation prediction model and demonstrated that the framework can generate reliable explanations that match domain knowledge.
Efficient Vision-Language Models by Summarizing Visual Tokens into Compact Registers
Authors: Yuxin Wen, Qingqing Cao, Qichen Fu, Sachin Mehta, Mahyar Najibi
Date: 2024-10-17 00:00:00
Tasks: Computational Efficiency

Recent advancements in vision-language models (VLMs) have expanded their potential for real-world applications, enabling these models to perform complex reasoning on images. In the widely used fully autoregressive transformer-based models like LLaVA, projected visual tokens are prepended to textual tokens. Oftentimes, visual tokens are significantly more than prompt tokens, resulting in increased computational overhead during both training and inference. In this paper, we propose Visual Compact Token Registers (Victor), a method that reduces the number of visual tokens by summarizing them into a smaller set of register tokens. Victor adds a few learnable register tokens after the visual tokens and summarizes the visual information into these registers using the first few layers in the language tower of VLMs. After these few layers, all visual tokens are discarded, significantly improving computational efficiency for both training and inference. Notably, our method is easy to implement and requires a small number of new trainable parameters with minimal impact on model performance. In our experiment, with merely 8 visual registers--about 1% of the original tokens--Victor shows less than a 4% accuracy drop while reducing the total training time by 43% and boosting the inference throughput by 3.3X.
FaceSaliencyAug: Mitigating Geographic, Gender and Stereotypical Biases via Saliency-Based Data Augmentation
Authors: Teerath Kumar, Alessandra Mileo, Malika Bendechache
Date: 2024-10-17 00:00:00
Tasks: Data Augmentation, Diversity, Fairness

Geographical, gender and stereotypical biases in computer vision models pose significant challenges to their performance and fairness. {In this study, we present an approach named FaceSaliencyAug aimed at addressing the gender bias in} {Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). Leveraging the salient regions} { of faces detected by saliency, the propose approach mitigates geographical and stereotypical biases } {in the datasets. FaceSaliencyAug} randomly selects masks from a predefined search space and applies them to the salient region of face images, subsequently restoring the original image with masked salient region. {The proposed} augmentation strategy enhances data diversity, thereby improving model performance and debiasing effects. We quantify dataset diversity using Image Similarity Score (ISS) across five datasets, including Flickr Faces HQ (FFHQ), WIKI, IMDB, Labelled Faces in the Wild (LFW), UTK Faces, and Diverse Dataset. The proposed approach demonstrates superior diversity metrics, as evaluated by ISS-intra and ISS-inter algorithms. Furthermore, we evaluate the effectiveness of our approach in mitigating gender bias on CEO, Engineer, Nurse, and School Teacher datasets. We use the Image-Image Association Score (IIAS) to measure gender bias in these occupations. Our experiments reveal a reduction in gender bias for both CNNs and ViTs, indicating the efficacy of our method in promoting fairness and inclusivity in computer vision models.
Provable Benefits of Complex Parameterizations for Structured State Space Models
Authors: Yuval Ran-Milo, Eden Lumbroso, Edo Cohen-Karlik, Raja Giryes, Amir Globerson, Nadav Cohen
Date: 2024-10-17 00:00:00
Tasks: Mamba, State Space Models

Structured state space models (SSMs), the core engine behind prominent neural networks such as S4 and Mamba, are linear dynamical systems adhering to a specified structure, most notably diagonal. In contrast to typical neural network modules, whose parameterizations are real, SSMs often use complex parameterizations. Theoretically explaining the benefits of complex parameterizations for SSMs is an open problem. The current paper takes a step towards its resolution, by establishing formal gaps between real and complex diagonal SSMs. Firstly, we prove that while a moderate dimension suffices in order for a complex SSM to express all mappings of a real SSM, a much higher dimension is needed for a real SSM to express mappings of a complex SSM. Secondly, we prove that even if the dimension of a real SSM is high enough to express a given mapping, typically, doing so requires the parameters of the real SSM to hold exponentially large values, which cannot be learned in practice. In contrast, a complex SSM can express any given mapping with moderate parameter values. Experiments corroborate our theory, and suggest a potential extension of the theory that accounts for selectivity, a new architectural feature yielding state of the art performance.
On Partial Prototype Collapse in the DINO Family of Self-Supervised Methods
Authors: Hariprasath Govindarajan, Per Sidén, Jacob Roll, Fredrik Lindsten
Date: 2024-10-17 00:00:00
Tasks: Self-Supervised Learning

A prominent self-supervised learning paradigm is to model the representations as clusters, or more generally as a mixture model. Learning to map the data samples to compact representations and fitting the mixture model simultaneously leads to the representation collapse problem. Regularizing the distribution of data points over the clusters is the prevalent strategy to avoid this issue. While this is sufficient to prevent full representation collapse, we show that a partial prototype collapse problem still exists in the DINO family of methods, that leads to significant redundancies in the prototypes. Such prototype redundancies serve as shortcuts for the method to achieve a marginal latent class distribution that matches the prescribed prior. We show that by encouraging the model to use diverse prototypes, the partial prototype collapse can be mitigated. Effective utilization of the prototypes enables the methods to learn more fine-grained clusters, encouraging more informative representations. We demonstrate that this is especially beneficial when pre-training on a long-tailed fine-grained dataset.
Towards Cross-Cultural Machine Translation with Retrieval-Augmented Generation from Multilingual Knowledge Graphs
Authors: Simone Conia, Daniel Lee, Min Li, Umar Farooq Minhas, Saloni Potdar, Yunyao Li
Date: 2024-10-17 00:00:00
Tasks: Knowledge Graphs, Machine Translation, Retrieval, Retrieval-augmented Generation, Translation, Transliteration, Word Translation

Translating text that contains entity names is a challenging task, as cultural-related references can vary significantly across languages. These variations may also be caused by transcreation, an adaptation process that entails more than transliteration and word-for-word translation. In this paper, we address the problem of cross-cultural translation on two fronts: (i) we introduce XC-Translate, the first large-scale, manually-created benchmark for machine translation that focuses on text that contains potentially culturally-nuanced entity names, and (ii) we propose KG-MT, a novel end-to-end method to integrate information from a multilingual knowledge graph into a neural machine translation model by leveraging a dense retrieval mechanism. Our experiments and analyses show that current machine translation systems and large language models still struggle to translate texts containing entity names, whereas KG-MT outperforms state-of-the-art approaches by a large margin, obtaining a 129% and 62% relative improvement compared to NLLB-200 and GPT-4, respectively.
From Isolated Conversations to Hierarchical Schemas: Dynamic Tree Memory Representation for LLMs
Authors: Alireza Rezazadeh, Zichao Li, Wei Wei, Yujia Bao
Date: 2024-10-17 00:00:00
Tasks: Dialogue Understanding, Management, Question Answering, Retrieval

Recent advancements in large language models have significantly improved their context windows, yet challenges in effective long-term memory management remain. We introduce MemTree, an algorithm that leverages a dynamic, tree-structured memory representation to optimize the organization, retrieval, and integration of information, akin to human cognitive schemas. MemTree organizes memory hierarchically, with each node encapsulating aggregated textual content, corresponding semantic embeddings, and varying abstraction levels across the tree's depths. Our algorithm dynamically adapts this memory structure by computing and comparing semantic embeddings of new and existing information to enrich the model's context-awareness. This approach allows MemTree to handle complex reasoning and extended interactions more effectively than traditional memory augmentation methods, which often rely on flat lookup tables. Evaluations on benchmarks for multi-turn dialogue understanding and document question answering show that MemTree significantly enhances performance in scenarios that demand structured memory management.
Best in Tau@LLMJudge: Criteria-Based Relevance Evaluation with Llama3
Authors: Naghmeh Farzi, Laura Dietz
Date: 2024-10-17 00:00:00
Tasks: Information Retrieval

Traditional evaluation of information retrieval (IR) systems relies on human-annotated relevance labels, which can be both biased and costly at scale. In this context, large language models (LLMs) offer an alternative by allowing us to directly prompt them to assign relevance labels for passages associated with each query. In this study, we explore alternative methods to directly prompt LLMs for assigned relevance labels, by exploring two hypotheses: Hypothesis 1 assumes that it is helpful to break down "relevance" into specific criteria - exactness, coverage, topicality, and contextual fit. We explore different approaches that prompt large language models (LLMs) to obtain criteria-level grades for all passages, and we consider various ways to aggregate criteria-level grades into a relevance label. Hypothesis 2 assumes that differences in linguistic style between queries and passages may negatively impact the automatic relevance label prediction. We explore whether improvements can be achieved by first synthesizing a summary of the passage in the linguistic style of a query, and then using this summary in place of the passage to assess its relevance. We include an empirical evaluation of our approaches based on data from the LLMJudge challenge run in Summer 2024, where our "Four Prompts" approach obtained the highest scores in Kendall's tau.
Latent Weight Diffusion: Generating reactive policies instead of trajectories
Authors: Shashank Hegde, Satyajeet Das, Gautam Salhotra, Gaurav S. Sukhatme
Date: 2024-10-17 00:00:00
Tasks: Denoising, Imitation Learning, Robot Manipulation

With the increasing availability of open-source robotic data, imitation learning has emerged as a viable approach for both robot manipulation and locomotion. Currently, large generalized policies are trained to predict controls or trajectories using diffusion models, which have the desirable property of learning multimodal action distributions. However, generalizability comes with a cost, namely, larger model size and slower inference. This is especially an issue for robotic tasks that require high control frequency. Further, there is a known trade-off between performance and action horizon for Diffusion Policy (DP), a popular model for generating trajectories: fewer diffusion queries accumulate greater trajectory tracking errors. For these reasons, it is common practice to run these models at high inference frequency, subject to robot computational constraints. To address these limitations, we propose Latent Weight Diffusion (LWD), a method that uses diffusion to generate closed-loop policies (weights for neural policies) for robotic tasks, rather than generating trajectories. Learning the behavior distribution through parameter space over trajectory space offers two key advantages: longer action horizons (fewer diffusion queries) & robustness to perturbations while retaining high performance; and a lower inference compute cost. To this end, we show that LWD has higher success rates than DP when the action horizon is longer and when stochastic perturbations exist in the environment. Furthermore, LWD achieves multitask performance comparable to DP while requiring just ~1/45th of the inference-time FLOPS
Generating Signed Language Instructions in Large-Scale Dialogue Systems
Authors: Mert İnan, Katherine Atwell, Anthony Sicilia, Lorna Quandt, Malihe Alikhani
Date: 2024-10-17 00:00:00
Tasks: Retrieval, Text Generation, Video Retrieval

We introduce a goal-oriented conversational AI system enhanced with American Sign Language (ASL) instructions, presenting the first implementation of such a system on a worldwide multimodal conversational AI platform. Accessible through a touch-based interface, our system receives input from users and seamlessly generates ASL instructions by leveraging retrieval methods and cognitively based gloss translations. Central to our design is a sign translation module powered by Large Language Models, alongside a token-based video retrieval system for delivering instructional content from recipes and wikiHow guides. Our development process is deeply rooted in a commitment to community engagement, incorporating insights from the Deaf and Hard-of-Hearing community, as well as experts in cognitive and ASL learning sciences. The effectiveness of our signing instructions is validated by user feedback, achieving ratings on par with those of the system in its non-signing variant. Additionally, our system demonstrates exceptional performance in retrieval accuracy and text-generation quality, measured by metrics such as BERTScore. We have made our codebase and datasets publicly accessible at https://github.com/Merterm/signed-dialogue, and a demo of our signed instruction video retrieval system is available at https://huggingface.co/spaces/merterm/signed-instructions.
Ensemble-based, large-eddy reconstruction of wind turbine inflow in a near-stationary atmospheric boundary layer through generative artificial intelligence
Authors: Alex Rybchuk, Luis A. Martínez-Tossas, Stefano Letizia, Nicholas Hamilton, Andy Scholbrock, Emina Maric, Daniel R. Houck, Thomas G. Herges, Nathaniel B. de Velder, Paula Doubrawa
Date: 2024-10-17 00:00:00

To validate the second-by-second dynamics of turbines in field experiments, it is necessary to accurately reconstruct the winds going into the turbine. Current time-resolved inflow reconstruction techniques estimate wind behavior in unobserved regions using relatively simple spectral-based models of the atmosphere. Here, we develop a technique for time-resolved inflow reconstruction that is rooted in a large-eddy simulation model of the atmosphere. Our "large-eddy reconstruction" technique blends observations and atmospheric model information through a diffusion model machine learning algorithm, allowing us to generate probabilistic ensembles of reconstructions for a single 10-min observational period. Our generated inflows can be used directly by aeroelastic codes or as inflow boundary conditions in a large-eddy simulation. We verify the second-by-second reconstruction capability of our technique in three synthetic field campaigns, finding positive Pearson correlation coefficient values (0.20>r>0.85) between ground-truth and reconstructed streamwise velocity, as well as smaller positive correlation coefficient values for unobserved fields (spanwise velocity, vertical velocity, and temperature). We validate our technique in three real-world case studies by driving large-eddy simulations with reconstructed inflows and comparing to independent inflow measurements. The reconstructions are visually similar to measurements, follow desired power spectra properties, and track second-by-second behavior (0.25 > r > 0.75).
Vision-Language-Action Model and Diffusion Policy Switching Enables Dexterous Control of an Anthropomorphic Hand
Authors: Cheng Pan, Kai Junge, Josie Hughes
Date: 2024-10-17 00:00:00
Tasks: Vision-Language-Action

To advance autonomous dexterous manipulation, we propose a hybrid control method that combines the relative advantages of a fine-tuned Vision-Language-Action (VLA) model and diffusion models. The VLA model provides language commanded high-level planning, which is highly generalizable, while the diffusion model handles low-level interactions which offers the precision and robustness required for specific objects and environments. By incorporating a switching signal into the training-data, we enable event based transitions between these two models for a pick-and-place task where the target object and placement location is commanded through language. This approach is deployed on our anthropomorphic ADAPT Hand 2, a 13DoF robotic hand, which incorporates compliance through series elastic actuation allowing for resilience for any interactions: showing the first use of a multi-fingered hand controlled with a VLA model. We demonstrate this model switching approach results in a over 80\% success rate compared to under 40\% when only using a VLA model, enabled by accurate near-object arm motion by the VLA model and a multi-modal grasping motion with error recovery abilities from the diffusion model.
Whisker-Inspired Tactile Sensing: A Sim2Real Approach for Precise Underwater Contact Tracking
Authors: Hao Li, Chengyi Xing, Saad Khan, Miaoya Zhong, Mark R. Cutkosky
Date: 2024-10-17 00:00:00

Aquatic mammals, such as pinnipeds, utilize their whiskers to detect and discriminate objects and analyze water movements, inspiring the development of robotic whiskers for sensing contacts, surfaces, and water flows. We present the design and application of underwater whisker sensors based on Fiber Bragg Grating (FBG) technology. These passive whiskers are mounted along the robot$'$s exterior to sense its surroundings through light, non-intrusive contacts. For contact tracking, we employ a sim-to-real learning framework, which involves extensive data collection in simulation followed by a sim-to-real calibration process to transfer the model trained in simulation to the real world. Experiments with whiskers immersed in water indicate that our approach can track contact points with an accuracy of $<2$ mm, without requiring precise robot proprioception. We demonstrate that the approach also generalizes to unseen objects.
On the Learn-to-Optimize Capabilities of Transformers in In-Context Sparse Recovery
Authors: Renpu Liu, Ruida Zhou, Cong Shen, Jing Yang
Date: 2024-10-17 00:00:00
Tasks: In-Context Learning

An intriguing property of the Transformer is its ability to perform in-context learning (ICL), where the Transformer can solve different inference tasks without parameter updating based on the contextual information provided by the corresponding input-output demonstration pairs. It has been theoretically proved that ICL is enabled by the capability of Transformers to perform gradient-descent algorithms (Von Oswald et al., 2023a; Bai et al., 2024). This work takes a step further and shows that Transformers can perform learning-to-optimize (L2O) algorithms. Specifically, for the ICL sparse recovery (formulated as LASSO) tasks, we show that a K-layer Transformer can perform an L2O algorithm with a provable convergence rate linear in K. This provides a new perspective explaining the superior ICL capability of Transformers, even with only a few layers, which cannot be achieved by the standard gradient-descent algorithms. Moreover, unlike the conventional L2O algorithms that require the measurement matrix involved in training to match that in testing, the trained Transformer is able to solve sparse recovery problems generated with different measurement matrices. Besides, Transformers as an L2O algorithm can leverage structural information embedded in the training tasks to accelerate its convergence during ICL, and generalize across different lengths of demonstration pairs, where conventional L2O algorithms typically struggle or fail. Such theoretical findings are supported by our experimental results.
RecoveryChaining: Learning Local Recovery Policies for Robust Manipulation
Authors: Shivam Vats, Devesh K. Jha, Maxim Likhachev, Oliver Kroemer, Diego Romeres
Date: 2024-10-17 00:00:00
Tasks: Hierarchical Reinforcement Learning

Model-based planners and controllers are commonly used to solve complex manipulation problems as they can efficiently optimize diverse objectives and generalize to long horizon tasks. However, they often fail during deployment due to noisy actuation, partial observability and imperfect models. To enable a robot to recover from such failures, we propose to use hierarchical reinforcement learning to learn a recovery policy. The recovery policy is triggered when a failure is detected based on sensory observations and seeks to take the robot to a state from which it can complete the task using the nominal model-based controllers. Our approach, called RecoveryChaining, uses a hybrid action space, where the model-based controllers are provided as additional \emph{nominal} options which allows the recovery policy to decide how to recover, when to switch to a nominal controller and which controller to switch to even with \emph{sparse rewards}. We evaluate our approach in three multi-step manipulation tasks with sparse rewards, where it learns significantly more robust recovery policies than those learned by baselines. We successfully transfer recovery policies learned in simulation to a physical robot to demonstrate the feasibility of sim-to-real transfer with our method.
MarineFormer: A Spatio-Temporal Attention Model for USV Navigation in Dynamic Marine Environments
Authors: Ehsan Kazemi, Iman Soltani
Date: 2024-10-17 00:00:00
Tasks: Collision Avoidance, Graph Attention, Reinforcement Learning (RL), Temporal Sequences

Navigating autonomously in marine environments including dynamic and static obstacles, and strong flow disturbances, such as in high-flow rivers, poses significant challenges for USVs. To address these challenges, we propose a novel methodology that leverages two types of attention: spatial attention, which learns to integrate diverse environmental factors and sensory information into navigation decisions, and temporal attention within a transformer framework to account for the dynamic, continuously changing nature of the environment. We devise MarineFormer, a Trans${\bf \text{former}}$-based navigation policy for dynamic ${\bf \text{Marine}}$ environments, trained end-to-end through reinforcement learning (RL). At its core, MarineFormer uses graph attention to capture spatial information and a transformer architecture to process temporal sequences in an environment that simulates a 2D turbulent marine condition involving multiple static and dynamic obstacles. We extensively evaluate the performance of the proposed method versus the state-of-the-art methods, as well as other classical planners. Our approach outperforms the state-of-the-art by nearly $20\%$ in episode completion success rate and additionally enhances the USV's path length efficiency.