pwc-archive/papers-with-abstracts huggingface dataset:
Multifidelity Simulation-based Inference for Computationally Expensive Simulators
Authors: Anastasia N. Krouglova, Hayden R. Johnson, Basile Confavreux, Michael Deistler, Pedro J. Gonçalves
Date: 2025-02-12 00:00:00
Tasks: Active Learning, Bayesian Inference, Transfer Learning

Across many domains of science, stochastic models are an essential tool to understand the mechanisms underlying empirically observed data. Models can be of different levels of detail and accuracy, with models of high-fidelity (i.e., high accuracy) to the phenomena under study being often preferable. However, inferring parameters of high-fidelity models via simulation-based inference is challenging, especially when the simulator is computationally expensive. We introduce MF-NPE, a multifidelity approach to neural posterior estimation that leverages inexpensive low-fidelity simulations to infer parameters of high-fidelity simulators within a limited simulation budget. MF-NPE performs neural posterior estimation with limited high-fidelity resources by virtue of transfer learning, with the ability to prioritize individual observations using active learning. On one statistical task with analytical ground-truth and two real-world tasks, MF-NPE shows comparable performance to current approaches while requiring up to two orders of magnitude fewer high-fidelity simulations. Overall, MF-NPE opens new opportunities to perform efficient Bayesian inference on computationally expensive simulators.
Systematic Knowledge Injection into Large Language Models via Diverse Augmentation for Domain-Specific RAG
Authors: Kushagra Bhushan, Yatin Nandwani, Dinesh Khandelwal, Sonam Gupta, Gaurav Pandey, Dinesh Raghu, Sachindra Joshi
Date: 2025-02-12 00:00:00
Tasks: RAG, Retrieval, Retrieval-augmented Generation

Retrieval-Augmented Generation (RAG) has emerged as a prominent method for incorporating domain knowledge into Large Language Models (LLMs). While RAG enhances response relevance by incorporating retrieved domain knowledge in the context, retrieval errors can still lead to hallucinations and incorrect answers. To recover from retriever failures, domain knowledge is injected by fine-tuning the model to generate the correct response, even in the case of retrieval errors. However, we observe that without systematic knowledge augmentation, fine-tuned LLMs may memorize new information but still fail to extract relevant domain knowledge, leading to poor performance. In this work, we present a novel framework that significantly enhances the fine-tuning process by augmenting the training data in two ways -- context augmentation and knowledge paraphrasing. In context augmentation, we create multiple training samples for a given QA pair by varying the relevance of the retrieved information, teaching the model when to ignore and when to rely on retrieved content. In knowledge paraphrasing, we fine-tune with multiple answers to the same question, enabling LLMs to better internalize specialized knowledge. To mitigate catastrophic forgetting due to fine-tuning, we add a domain-specific identifier to a question and also utilize a replay buffer containing general QA pairs. Experimental results demonstrate the efficacy of our method over existing techniques, achieving up to 10\% relative gain in token-level recall while preserving the LLM's generalization capabilities.
FloVD: Optical Flow Meets Video Diffusion Model for Enhanced Camera-Controlled Video Synthesis
Authors: Wonjoon Jin, Qi Dai, Chong Luo, Seung-Hwan Baek, Sunghyun Cho
Date: 2025-02-12 00:00:00
Proceeding: CVPR 2025 1
Tasks: Motion Synthesis, Optical Flow Estimation, Video Generation

This paper presents FloVD, a novel optical-flow-based video diffusion model for camera-controllable video generation. FloVD leverages optical flow maps to represent motions of the camera and moving objects. This approach offers two key benefits. Since optical flow can be directly estimated from videos, our approach allows for the use of arbitrary training videos without ground-truth camera parameters. Moreover, as background optical flow encodes 3D correlation across different viewpoints, our method enables detailed camera control by leveraging the background motion. To synthesize natural object motion while supporting detailed camera control, our framework adopts a two-stage video synthesis pipeline consisting of optical flow generation and flow-conditioned video synthesis. Extensive experiments demonstrate the superiority of our method over previous approaches in terms of accurate camera control and natural object motion synthesis.
Plantation Monitoring Using Drone Images: A Dataset and Performance Review
Authors: Yashwanth Karumanchi, Gudala Laxmi Prasanna, Snehasis Mukherjee, Nagesh Kolagani
Date: 2025-02-12 00:00:00
Tasks: object-detection, Object Detection

Automatic monitoring of tree plantations plays a crucial role in agriculture. Flawless monitoring of tree health helps farmers make informed decisions regarding their management by taking appropriate action. Use of drone images for automatic plantation monitoring can enhance the accuracy of the monitoring process, while still being affordable to small farmers in developing countries such as India. Small, low cost drones equipped with an RGB camera can capture high-resolution images of agricultural fields, allowing for detailed analysis of the well-being of the plantations. Existing methods of automated plantation monitoring are mostly based on satellite images, which are difficult to get for the farmers. We propose an automated system for plantation health monitoring using drone images, which are becoming easier to get for the farmers. We propose a dataset of images of trees with three categories: ``Good health", ``Stunted", and ``Dead". We annotate the dataset using CVAT annotation tool, for use in research purposes. We experiment with different well-known CNN models to observe their performance on the proposed dataset. The initial low accuracy levels show the complexity of the proposed dataset. Further, our study revealed that, depth-wise convolution operation embedded in a deep CNN model, can enhance the performance of the model on drone dataset. Further, we apply state-of-the-art object detection models to identify individual trees to better monitor them automatically.
EventEgo3D++: 3D Human Motion Capture from a Head-Mounted Event Camera
Authors: Christen Millerdurai, Hiroyasu Akada, Jian Wang, Diogo Luvizon, Alain Pagani, Didier Stricker, Christian Theobalt, Vladislav Golyanik
Date: 2025-02-11 00:00:00

Monocular egocentric 3D human motion capture remains a significant challenge, particularly under conditions of low lighting and fast movements, which are common in head-mounted device applications. Existing methods that rely on RGB cameras often fail under these conditions. To address these limitations, we introduce EventEgo3D++, the first approach that leverages a monocular event camera with a fisheye lens for 3D human motion capture. Event cameras excel in high-speed scenarios and varying illumination due to their high temporal resolution, providing reliable cues for accurate 3D human motion capture. EventEgo3D++ leverages the LNES representation of event streams to enable precise 3D reconstructions. We have also developed a mobile head-mounted device (HMD) prototype equipped with an event camera, capturing a comprehensive dataset that includes real event observations from both controlled studio environments and in-the-wild settings, in addition to a synthetic dataset. Additionally, to provide a more holistic dataset, we include allocentric RGB streams that offer different perspectives of the HMD wearer, along with their corresponding SMPL body model. Our experiments demonstrate that EventEgo3D++ achieves superior 3D accuracy and robustness compared to existing solutions, even in challenging conditions. Moreover, our method supports real-time 3D pose updates at a rate of 140Hz. This work is an extension of the EventEgo3D approach (CVPR 2024) and further advances the state of the art in egocentric 3D human motion capture. For more details, visit the project page at https://eventego3d.mpi-inf.mpg.de.
$\texttt{LucidAtlas}$: Learning Uncertainty-Aware, Covariate-Disentangled, Individualized Atlas Representations
Authors: Yining Jiao, Sreekalyani Bhamidi, Huaizhi Qu, Carlton Zdanski, Julia Kimbell, Andrew Prince, Cameron Worden, Samuel Kirse, Christopher Rutter, Benjamin Shields, William Dunn, Jisan Mahmud, Tianlong Chen, Marc Niethammer
Date: 2025-02-12 00:00:00
Tasks: Additive models, scientific discovery

The goal of this work is to develop principled techniques to extract information from high dimensional data sets with complex dependencies in areas such as medicine that can provide insight into individual as well as population level variation. We develop $\texttt{LucidAtlas}$, an approach that can represent spatially varying information, and can capture the influence of covariates as well as population uncertainty. As a versatile atlas representation, $\texttt{LucidAtlas}$ offers robust capabilities for covariate interpretation, individualized prediction, population trend analysis, and uncertainty estimation, with the flexibility to incorporate prior knowledge. Additionally, we discuss the trustworthiness and potential risks of neural additive models for analyzing dependent covariates and then introduce a marginalization approach to explain the dependence of an individual predictor on the models' response (the atlas). To validate our method, we demonstrate its generalizability on two medical datasets. Our findings underscore the critical role of by-construction interpretable models in advancing scientific discovery. Our code will be publicly available upon acceptance.
Quality-Aware Decoding: Unifying Quality Estimation and Decoding
Authors: Sai Koneru, Matthias Huck, Miriam Exel, Jan Niehues
Date: 2025-02-12 00:00:00
Tasks: Decoder, Document Translation, NMT, Re-Ranking, Translation

An emerging research direction in NMT involves the use of Quality Estimation (QE) models, which have demonstrated high correlations with human judgment and can enhance translations through Quality-Aware Decoding. Although several approaches have been proposed based on sampling multiple candidate translations, none have integrated these models directly into the decoding process. In this paper, we address this by proposing a novel token-level QE model capable of reliably scoring partial translations. We build a uni-directional QE model for this, as decoder models are inherently trained and efficient on partial sequences. We then present a decoding strategy that integrates the QE model for Quality-Aware decoding and demonstrate that the translation quality improves when compared to the N-best list re-ranking with state-of-the-art QE models (upto $1.39$ XCOMET-XXL $\uparrow$). Finally, we show that our approach provides significant benefits in document translation tasks, where the quality of N-best lists is typically suboptimal.
LM2: Large Memory Models
Authors: Jikun Kang, Wenqi Wu, Filippos Christianos, Alex J. Chan, Fraser Greenlee, George Thomas, Marvin Purtorab, Andy Toulis
Date: 2025-02-09 00:00:00
Tasks: Decoder, MMLU, Question Answering

This paper introduces the Large Memory Model (LM2), a decoder-only Transformer architecture enhanced with an auxiliary memory module that aims to address the limitations of standard Transformers in multi-step reasoning, relational argumentation, and synthesizing information distributed over long contexts. The proposed LM2 incorporates a memory module that acts as a contextual representation repository, interacting with input tokens via cross attention and updating through gating mechanisms. To preserve the Transformers general-purpose capabilities, LM2 maintains the original information flow while integrating a complementary memory pathway. Experimental results on the BABILong benchmark demonstrate that the LM2model outperforms both the memory-augmented RMT model by 37.1% and the baseline Llama-3.2 model by 86.3% on average across tasks. LM2 exhibits exceptional capabilities in multi-hop inference, numerical reasoning, and large-context question-answering. On the MMLU dataset, it achieves a 5.0% improvement over a pre-trained vanilla model, demonstrating that its memory module does not degrade performance on general tasks. Further, in our analysis, we explore the memory interpretability, effectiveness of memory modules, and test-time behavior. Our findings emphasize the importance of explicit memory in enhancing Transformer architectures.
Graph rewiring and preprocessing for graph neural networks based on effective resistance
Authors: Xu Shen, Pietro Lio, Lintao Yang, Ru Yuan, Yuyang Zhang, Chengbin Peng
Date: 2024-05-07 00:00:00
Proceeding: IEEE Transactions on Knowledge and Data Engineering ( Volume: 36, Issue: 11, November 2024) 2024 5

Graph neural networks (GNNs) are powerful models for processing graph data and have demonstrated state-of-the-art performance on many downstream tasks. However, existing GNNs can generally suffer from two limitations: over-smoothing and over-squashing, which can significantly undermine their learning ability for large graphs. To overcome these issues simultaneously, by utilizing the concept of effective resistances, we focus on minimizing total constrained resistance while identifying problematic edges using topological redundancy and bottleneck sparsity coefficients. We introduce a novel graph rewiring and preprocessing method  guided by effective resistance (GPER), capable of edge addition or removal. Theoretical analysis validates our method's efficacy in mitigating over-smoothing and over-squashing. {In the experiments, we conduct node and graph classifications on the benchmark datasets and can achieve an average improvement of $7.8\%$ and $2.0\%$, respectively. We also conduct scalability analysis on large graphs with GCN and demonstrate that the proposed preprocess approach can reduce graph size by over $50\%$ while improve the performance.
Preconditioned inexact fixed point iteration method for solving tensor absolute value equation
Authors: Xin-Mei Lv ， Shu-Xin Miao
Date: 2024-11-08 00:00:00
Proceeding: 2024/8/17 2024 11

Preconditioning techniques are the most used methods to accelerate the tensor splitting iteration method for solving multi-linear systems. In this paper, we consider the
numerical method for solving tensor absolute value equation based on preconditioned
techniques and the inexact fixed point iteration method. We present a general form of
preconditioned inexact fixed point iteration method for solving tensor absolute value
equation. Theoretically, we give the convergence of the proposed method. Numerically, we show the efficiency of the proposed method by three numerical examples.
Solving tensor absolute value equation
Authors: Ｍａ Ｃｈａｎｇｆｅ ｎｇ ，Ｘｉ ｅ Ｙａｊ ｕｎ
Date: 2022-03-07 00:00:00
Proceeding: 2022/3/7 2022 3

Ｉｎ ｔ ｈｉ ｓ ｐａｐ ｅｒ ，
ｔ ｈｅ ｔ ｅｎｓ ｏｒ ａｂｓ ｏｌ ｕｔ ｅ ｖａｌｕｅ ｅｑｕａｔ ｉ ｏｎ ｉ ｓ ｒ ｅｃｏｎｓ ｔ ｒ ｕｃ ｔ ｅｄ ｉ ｎｔ ｏ
ｔ ｅ ｎｓ ｏｒ ｃｏｍｐｌ ｅｍｅｎｔ ａｒｙ ｐｒ ｏｂｌ ｅｍ ｂｙ ｉ ｎｔ ｒ ｏ ｄｕｃｉ ｎｇ ｃ ｏｍｐｌ ｅｍｅｎｔ ａｒｙ ｆｕｎｃ ｔ ｉ ｏｎ ， ａｎｄ ｉ ｔ ｓ
ｋｅｙ ｐｒｏｐ ｅｒ ｔ ｉ ｅｓ ａｒ ｅ ａｎａｌ ｙｚｅｄ ． Ｆｏｒ ｔ ｈｅ ｃｏｎｓｔ ｒ ｕｃｔ ｅｄ ｔ ｅｎｓ ｏｒ ｃｏｍｐｌ ｅｍｅｎｔ ａｒｙ ｐｒ ｏｂｌ ｅｍ，
ａ ｃｏｎｔ ｉ ｎｕｏｕｓ
－
ｔ ｉ ｍｅ ｎｅｕｒ ａｌ ｎｅｔｗｏｒ ｋ ｍｅｔ ｈｏ ｄ （ ＣＴＮＮ）
ｗａｓ ｐｒ ｏｐｏｓ ｅｄ ａｎｄ ｔ ｈｅ ｃｏｎｖｅｒ
？
ｇｅ ｎｃｅ ｔ ｈｅｏｒ ｅｍ ｉ ｓ ｐ ｒ ｏｖｅｄ ． Ｓ ｏｍｅ ｎｕｍｅｒ ｉ ｃ ａｌ ｒ ｅｓ ｕｌ ｔ ｓ ｗｅｒ ｅ ｒｅｐｏｒ ｔ ｅｄ ． Ｎｕｍｅｒ ｉ ｃａｌ ｒ ｅ ｓ ｕｌ ｔ ｓ
ｓ ｈｏｗ ｔ ｈａｔ ｔ ｈｅ ｐｒ ｏｐｏｓ ｅｄ ａｌ ｇｏｒｉ ｔ ｈｍ ｉ ｓ ｒ ｏｂｕｓ ｔ ａｎｄ ｅｆｅｃｔ ｉ ｖｅ
GS-CPR: Efficient Camera Pose Refinement via 3D Gaussian Splatting
Authors: Changkun Liu, Shuai Chen, Yash Sanjay Bhalgat, Siyan Hu, Ming Cheng, ZiRui Wang, Victor Adrian Prisacariu, Tristan Braud
Date: 2025-01-23 00:00:00
Proceeding: International Conference on Learning Representations (ICLR) 2025 1
Tasks: 3DGS, NeRF, Pose Estimation, regression, Visual Localization

We leverage 3D Gaussian Splatting (3DGS) as a scene representation and propose a novel test-time camera pose refinement (CPR) framework, GS-CPR. This framework enhances the localization accuracy of state-of-the-art absolute pose regression and scene coordinate regression methods. The 3DGS model renders high-quality synthetic images and depth maps to facilitate the establishment of 2D-3D correspondences. GS-CPR obviates the need for training feature extractors or descriptors by operating directly on RGB images, utilizing the 3D foundation model, MASt3R, for precise 2D matching. To improve the robustness of our model in challenging outdoor environments, we incorporate an exposure-adaptive module within the 3DGS framework. Consequently, GS-CPR enables efficient one-shot pose refinement given a single RGB query and a coarse initial pose estimation. Our proposed approach surpasses leading NeRF-based optimization methods in both accuracy and runtime across indoor and outdoor visual localization benchmarks, achieving new state-of-the-art accuracy on two indoor datasets.
Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling
Authors: Runze Liu, Junqi Gao, Jian Zhao, Kaiyan Zhang, Xiu Li, Biqing Qi, Wanli Ouyang, BoWen Zhou
Date: 2025-02-10 00:00:00
Tasks: Math

Test-Time Scaling (TTS) is an important method for improving the performance of Large Language Models (LLMs) by using additional computation during the inference phase. However, current studies do not systematically analyze how policy models, Process Reward Models (PRMs), and problem difficulty influence TTS. This lack of analysis limits the understanding and practical use of TTS methods. In this paper, we focus on two core questions: (1) What is the optimal approach to scale test-time computation across different policy models, PRMs, and problem difficulty levels? (2) To what extent can extended computation improve the performance of LLMs on complex tasks, and can smaller language models outperform larger ones through this approach? Through comprehensive experiments on MATH-500 and challenging AIME24 tasks, we have the following observations: (1) The compute-optimal TTS strategy is highly dependent on the choice of policy model, PRM, and problem difficulty. (2) With our compute-optimal TTS strategy, extremely small policy models can outperform larger models. For example, a 1B LLM can exceed a 405B LLM on MATH-500. Moreover, on both MATH-500 and AIME24, a 0.5B LLM outperforms GPT-4o, a 3B LLM surpasses a 405B LLM, and a 7B LLM beats o1 and DeepSeek-R1, while with higher inference efficiency. These findings show the significance of adapting TTS strategies to the specific characteristics of each task and model and indicate that TTS is a promising approach for enhancing the reasoning abilities of LLMs.
semantic image synthesis of anime characters based on conditional generative adversarial networks
Authors: Xuhui Zhu; Feng Jiang; Jing Wen; Yi Wang; Qiang Gao
Date: 2024-11-25 00:00:00
Proceeding: British Machine Vision Conference 2024 11
Tasks: Edge Detection, Generative Adversarial Network, Image Generation, Semantic Segmentation

The goal of semantic image synthesis is to generate realistic images from semantic label maps. However, current approaches for generating anime characters from semantic label maps still encounter some issues, particularly the inability to directly generate a specific anime character from the semantic label map, as well as blurred colors and chaotic textures in the generated images. To address these issues, we propose a Conditional Generative Adversarial Network for Semantic Image Synthesis of Anime Characters. Specifically, in the generator, we propose character identity tensor to control the generation of specified anime characters, and introduce conditional noise to enable the generated images to have natural colors. Additionally, we redesign the discriminator as a network based on semantic segmentation and edge detection, which effectively supervises the texture details, guiding the generator to generate images with higher-quality textures. Experimental results show the superiority of our proposed method in generating specific and realistic anime characters compared to existing methods.
Performance Analysis of Traditional VQA Models Under Limited Computational Resources
Authors: Jihao Gu
Date: 2025-02-09 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

In real-world applications where computational resources are limited, effectively integrating visual and textual information for Visual Question Answering (VQA) presents significant challenges. This paper investigates the performance of traditional models under computational constraints, focusing on enhancing VQA performance, particularly for numerical and counting questions. We evaluate models based on Bidirectional GRU (BidGRU), GRU, Bidirectional LSTM (BidLSTM), and Convolutional Neural Networks (CNN), analyzing the impact of different vocabulary sizes, fine-tuning strategies, and embedding dimensions. Experimental results show that the BidGRU model with an embedding dimension of 300 and a vocabulary size of 3000 achieves the best overall performance without the computational overhead of larger models. Ablation studies emphasize the importance of attention mechanisms and counting information in handling complex reasoning tasks under resource limitations. Our research provides valuable insights for developing more efficient VQA models suitable for deployment in environments with limited computational capacity.
Harmonic Loss Trains Interpretable AI Models
Authors: David D. Baek, Ziming Liu, Riya Tyagi, Max Tegmark
Date: 2025-02-03 00:00:00
Tasks: Efficient Neural Network

In this paper, we introduce **harmonic loss** as an alternative to the standard cross-entropy loss for training neural networks and large language models (LLMs). Harmonic loss enables improved interpretability and faster convergence, owing to its scale invariance and finite convergence point by design, which can be interpreted as a class center. We first validate the performance of harmonic models across algorithmic, vision, and language datasets. Through extensive experiments, we demonstrate that models trained with harmonic loss outperform standard models by: (a) enhancing interpretability, (b) requiring less data for generalization, and (c) reducing grokking. Moreover, we compare a GPT-2 model trained with harmonic loss to the standard GPT-2, illustrating that the harmonic model develops more interpretable representations. Looking forward, we believe harmonic loss has the potential to become a valuable tool in domains with limited data availability or in high-stakes applications where interpretability and reliability are paramount, paving the way for more robust and efficient neural network models.
MM-IQ: Benchmarking Human-Like Abstraction and Reasoning in Multimodal Models
Authors: Huanqia Cai, Yijun Yang, Winston Hu
Date: 2025-02-02 00:00:00
Tasks: Benchmarking

IQ testing has served as a foundational methodology for evaluating human cognitive capabilities, deliberately decoupling assessment from linguistic background, language proficiency, or domain-specific knowledge to isolate core competencies in abstraction and reasoning. Yet, artificial intelligence research currently lacks systematic benchmarks to quantify these critical cognitive dimensions in multimodal systems. To address this critical gap, we propose MM-IQ, a comprehensive evaluation framework comprising 2,710 meticulously curated test items spanning 8 distinct reasoning paradigms. Through systematic evaluation of leading open-source and proprietary multimodal models, our benchmark reveals striking limitations: even state-of-the-art architectures achieve only marginally superior performance to random chance (27.49% vs. 25% baseline accuracy). This substantial performance chasm highlights the inadequacy of current multimodal systems in approximating fundamental human reasoning capacities, underscoring the need for paradigm-shifting advancements to bridge this cognitive divide.