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OpenReview | ICLR | 2,026 | FreqKV: Key-Value Compression in Frequency Domain for Context Window Extension | Existing key-value (KV) cache compression methods for large language models (LLMs) often rely on token eviction, which risks losing critical local information in both long prefilling and decoding scenarios. When extrapolating beyond the pretrained context length, their performance degrades sharply on long-context bench... | Large Language Models, KV Compression, Context Extension | foundation or frontier models, including LLMs | This paper introduces FreqKV, an efficient context extension method that iteratively compresses key-value states in the frequency domain. | [
4,
6,
4
] | Accept (Poster) | Jushi Kai, Yixuan Wang, Boyi Zeng, Haoli Bai, Bo Jiang, Ziwei He, Zhouhan Lin | ~Jushi_Kai1, ~Yixuan_Wang10, ~Boyi_Zeng2, ~Haoli_Bai2, ~Bo_Jiang2, ~Ziwei_He1, ~Zhouhan_Lin1 | 20250918 | https://openreview.net/forum?id=wFSOtyvQ9d | wFSOtyvQ9d | @inproceedings{
kai2026freqkv,
title={Freq{KV}: Key-Value Compression in Frequency Domain for Context Window Extension},
author={Jushi Kai and Yixuan Wang and Boyi Zeng and Haoli Bai and Bo Jiang and Ziwei He and Zhouhan Lin},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},... | OpenReview/ICLR/figures/2026/accept_poster/wFSOtyvQ9d/Figure3.png | 3 | Figure 3: The overview of our FreqKV. (a) The illustration of the frequency-domain compression. (b) The KV cache will be compressed in an iterative manner to extend the context window. Sink tokens remain uncompressed throughout the process. The tokens after sink tokens will be compressed in the frequency domain and sub... | <paragraph_1>To reduce redundancy in the key-value (KV) cache, we compress KV states in the frequency domain as shown in Figure 3a. Specifically, we conduct DCT along the sequence dimension to transfer the KV cache to the frequency domain:</paragraph_1>
<paragraph_2>Extending the context window of LLMs is fundamentally... | diagram | 0.899471 |
OpenReview | ICLR | 2,026 | ThinkOmni: Lifting Textual Reasoning to Omni-modal Scenarios via Guidance Decoding | Omni-modal reasoning is essential for intelligent systems to understand and draw inferences from diverse data sources. While existing omni-modal large language models (OLLM) excel at perceiving diverse modalities, they lack the complex reasoning abilities of recent large reasoning models (LRM). However, enhancing the r... | Omni-modal large language models, training-free guidance decoding, language model reasoning | applications to computer vision, audio, language, and other modalities | [
6,
6,
6,
6
] | Accept (Poster) | Yiran Guan, Sifan Tu, Dingkang Liang, Linghao Zhu, Jianzhong Ju, Zhenbo Luo, Jian Luan, Yuliang Liu, Xiang Bai | ~Yiran_Guan1, ~Sifan_Tu2, ~Dingkang_Liang2, ~Linghao_Zhu1, ~Jianzhong_Ju1, ~Zhenbo_Luo2, ~Jian_Luan1, ~Yuliang_Liu2, ~Xiang_Bai1 | 20250917 | https://openreview.net/forum?id=pMpCOjzwI1 | pMpCOjzwI1 | @inproceedings{
guan2026thinkomni,
title={ThinkOmni: Lifting Textual Reasoning to Omni-modal Scenarios via Guidance Decoding},
author={Yiran Guan and Sifan Tu and Dingkang Liang and Linghao Zhu and Jianzhong Ju and Zhenbo Luo and Jian Luan and Yuliang Liu and Xiang Bai},
booktitle={The Fourteenth International Conferen... | OpenReview/ICLR/figures/2026/accept_poster/pMpCOjzwI1/Figure3.png | 3 | Figure 3: Guidance decoding methods. “Guid.” denotes the guiding model, and “Amat.” denotes the amateur model. | <paragraph_1>In Contrastive Decoding (Fig. 3(a)), the contrastive pair is formed by comparing the responses to the same prompt from the original guiding model and an additional amateur model, with z+ set to zbase. In Visual Contrastive Decoding (Fig. 3(b)), the contrastive pair is created by applying different input co... | diagram | 0.93543 | |
OpenReview | ICLR | 2,026 | Task-Agnostic Amortized Multi-Objective Optimization | Balancing competing objectives is omnipresent across disciplines, from drug design to autonomous systems. Multi-objective Bayesian optimization is a promising solution for such expensive, black-box problems: it fits probabilistic surrogates and selects new designs via an acquisition function that balances exploration a... | Multi-Objective Optimization, Bayesian Optimization, Transformers, Neural Processes | probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.) | We introduce a fully amortized (surrogate model + acquisition function), dimension-agnostic policy for multi-objective optimization. | [
6,
6,
8,
4
] | Accept (Poster) | Xinyu Zhang, Conor Hassan, Julien Martinelli, Daolang Huang, Samuel Kaski | ~Xinyu_Zhang41, ~Conor_Hassan1, ~Julien_Martinelli1, ~Daolang_Huang1, ~Samuel_Kaski1 | 20250920 | https://openreview.net/forum?id=odmeUlWta8 | odmeUlWta8 | @inproceedings{
zhang2026taskagnostic,
title={Task-Agnostic Amortized Multi-Objective Optimization},
author={Xinyu Zhang and Conor Hassan and Julien Martinelli and Daolang Huang and Samuel Kaski},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/f... | OpenReview/ICLR/figures/2026/accept_poster/odmeUlWta8/Figure2.png | 2 | Figure 2: Dimension-agnostic embedder for a single observation. | <paragraph_1>(I) Dimension-agnostic embedder. We apply learnable scalar-to-vector maps ex : R →Rde and ey : R →Rde dimension-wise, resulting in ex = ex(x) ∈Rdτ x×de and ey = ey(y) ∈Rdτ y×de. Both functions ex and ey are parameterized as feedforward neural networks. After L transformer layers on the concatenated tokens ... | diagram | 0.99614 |
OpenReview | ICLR | 2,026 | DESIGNER: Design-Logic-Guided Multidisciplinary Data Synthesis for LLM Reasoning | Large language models (LLMs) perform strongly on many language tasks but still struggle with complex multi-step reasoning across disciplines. Existing reasoning datasets often lack disciplinary breadth, reasoning depth, and diversity, as well as guiding principles for question synthesis. We propose DESIGNER: a DESIGN-l... | Large Language Models, Data Synthesis, Synthetic Data, Reasoning, Post-Training, Supervised Fine-Tuning | datasets and benchmarks | [
6,
4,
2,
8,
4
] | Accept (Poster) | Weize Liu, Yongchi Zhao, Yijia Luo, Mingyu Xu, Jiaheng Liu, Yanan Li, Xiguo Hu, ZhiqiBai, Yuchi Xu, Wenbo Su, Bo Zheng | ~Weize_Liu1, ~Yongchi_Zhao1, ~Yijia_Luo1, ~Mingyu_Xu3, ~Jiaheng_Liu1, ~Yanan_Li8, ~Xiguo_Hu1, ~ZhiqiBai1, ~Yuchi_Xu1, ~Wenbo_Su2, ~Bo_Zheng5 | 20250903 | https://openreview.net/forum?id=SQVxBJhIrK | SQVxBJhIrK | @inproceedings{
liu2026designer,
title={{DESIGNER}: Design-Logic-Guided Multidisciplinary Data Synthesis for {LLM} Reasoning},
author={Weize Liu and Yongchi Zhao and Yijia Luo and Mingyu Xu and Jiaheng Liu and Yanan Li and Xiguo Hu and ZhiqiBai and Yuchi Xu and Wenbo Su and Bo Zheng},
booktitle={The Fourteenth Internat... | OpenReview/ICLR/figures/2026/accept_poster/SQVxBJhIrK/Figure2.png | 2 | Figure 2: The Design-Logic-Guided Multidisciplinary Data Synthesis Pipeline. | <paragraph_1>Specifically, our pipeline is illustrated in Figure 2. First, we process large-scale book and web corpora with multi-dimensional labeling and filtering (discipline, readability, educational value, reasoning depth) to construct a high-quality source material library. From a question bank of hundreds of mill... | diagram | 0.99595 | |
OpenReview | ICLR | 2,026 | DESIGNER: Design-Logic-Guided Multidisciplinary Data Synthesis for LLM Reasoning | Large language models (LLMs) perform strongly on many language tasks but still struggle with complex multi-step reasoning across disciplines. Existing reasoning datasets often lack disciplinary breadth, reasoning depth, and diversity, as well as guiding principles for question synthesis. We propose DESIGNER: a DESIGN-l... | Large Language Models, Data Synthesis, Synthetic Data, Reasoning, Post-Training, Supervised Fine-Tuning | datasets and benchmarks | [
6,
4,
2,
8,
4
] | Accept (Poster) | Weize Liu, Yongchi Zhao, Yijia Luo, Mingyu Xu, Jiaheng Liu, Yanan Li, Xiguo Hu, ZhiqiBai, Yuchi Xu, Wenbo Su, Bo Zheng | ~Weize_Liu1, ~Yongchi_Zhao1, ~Yijia_Luo1, ~Mingyu_Xu3, ~Jiaheng_Liu1, ~Yanan_Li8, ~Xiguo_Hu1, ~ZhiqiBai1, ~Yuchi_Xu1, ~Wenbo_Su2, ~Bo_Zheng5 | 20250903 | https://openreview.net/forum?id=SQVxBJhIrK | SQVxBJhIrK | @inproceedings{
liu2026designer,
title={{DESIGNER}: Design-Logic-Guided Multidisciplinary Data Synthesis for {LLM} Reasoning},
author={Weize Liu and Yongchi Zhao and Yijia Luo and Mingyu Xu and Jiaheng Liu and Yanan Li and Xiguo Hu and ZhiqiBai and Yuchi Xu and Wenbo Su and Bo Zheng},
booktitle={The Fourteenth Internat... | OpenReview/ICLR/figures/2026/accept_poster/SQVxBJhIrK/Figure22.png | 22 | Figure 22: An example of the Design Logic for a Mathematics problem, showing the Mermaid source code (a) and the corresponding visual flowchart (b). | diagram | 0.907912 | ||
OpenReview | ICLR | 2,026 | Enhancing Multivariate Time Series Forecasting with Global Temporal Retrieval | Multivariate time series forecasting (MTSF) plays a vital role in numerous real-world applications, yet existing models remain constrained by their reliance on a limited historical context. This limitation prevents them from effectively capturing global periodic patterns that often span cycles significantly longer than... | Time-series forecasting, model plugins | learning on time series and dynamical systems | A lightweight, model-agnostic plug-and-play module for time-series forecasting models. | [
6,
4,
4,
8
] | Accept (Poster) | Fanpu Cao, Lu Dai, Jindong Han, Hui Xiong | ~Fanpu_Cao1, ~Lu_Dai1, ~Jindong_Han1, ~Hui_Xiong1 | 20250915 | https://openreview.net/forum?id=QUJBPSfyui | QUJBPSfyui | @inproceedings{
cao2026enhancing,
title={Enhancing Multivariate Time Series Forecasting with Global Temporal Retrieval},
author={Fanpu Cao and Lu Dai and Jindong Han and Hui Xiong},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=QUJBPSf... | OpenReview/ICLR/figures/2026/accept_poster/QUJBPSfyui/Figure2.png | 2 | Figure 2: Overview of the Global Temporal Retriever (GTR): a plug-and-play module compatible with any MTSF forecaster. GTR operates in three stages: (1) retrieves corresponding segments from global temporal embedding; (2) aligns them with the input and uses 2D convolution to jointly model local and global periodicity; ... | <paragraph_1>Method Overview. In this paper, we propose the Global Temporal Retriever (GTR) — a lightweight, plug-and-play module designed to extend a model’s temporal receptive field beyond the immediate input window. As illustrated in Figure 2, the proposed method operates in two phases: (1) The GTR module enhances g... | diagram | 0.993829 |
OpenReview | ICLR | 2,026 | From EduVisBench to EduVisAgent: A Benchmark and Multi-Agent Framework for Reasoning-Driven Pedagogical Visualization | While foundation models (FMs), such as diffusion models and large vision-language models (LVLMs), have been widely applied in educational contexts, their ability to generate pedagogically effective visual explanations remains limited. Most existing approaches focus primarily on textual reasoning, overlooking the critic... | education, agent, benchmark, llm, application, visualisation | datasets and benchmarks | [
6,
2,
2,
6,
6
] | Accept (Poster) | Haonian Ji, Shi Qiu, Siyang Xin, Siwei Han, Zhaorun Chen, Dake Zhang, Hongyi Wang, Huaxiu Yao | ~Haonian_Ji1, ~Shi_Qiu2, ~Siyang_Xin1, ~Siwei_Han1, ~Zhaorun_Chen1, ~Dake_Zhang3, ~Hongyi_Wang1, ~Huaxiu_Yao1 | 20250918 | https://openreview.net/forum?id=FVCpV04ZRe | FVCpV04ZRe | @inproceedings{
ji2026from,
title={From EduVisBench to EduVisAgent: A Benchmark and Multi-Agent Framework for Reasoning-Driven Pedagogical Visualization},
author={Haonian Ji and Shi Qiu and Siyang Xin and Siwei Han and Zhaorun Chen and Dake Zhang and Hongyi Wang and Huaxiu Yao},
booktitle={The Fourteenth International ... | OpenReview/ICLR/figures/2026/accept_poster/FVCpV04ZRe/Figure4.png | 4 | Figure 4: Workflow for evaluation. | <paragraph_1>Evaluation Protocol. As shown in Figure 4, models are provided with a visualization prompt together with a question and are asked to generate visual outputs. To enable fair comparison across heterogeneous outputs, we first canonicalize every model result to a raster image prior to scoring. This standardizat... | diagram | 0.932038 | |
OpenReview | ICLR | 2,026 | A State-Transition Framework for Efficient LLM Reasoning | While Long Chain-of-Thought (CoT) reasoning significantly improves Large Language Models (LLMs) performance on complex reasoning tasks, the substantial computational and memory costs of generating long CoT sequences limit their efficiency and practicality.
Existing studies usually enhance the reasoning efficiency of LL... | Large Language Models, reasoning, efficient reasoning | foundation or frontier models, including LLMs | [
4,
6,
6,
6
] | Accept (Poster) | Liang Zhang, Yu Zhao, Longyue Wang, Tianqi Shi, Weihua Luo, Kaifu Zhang, Jinsong Su | ~Liang_Zhang9, ~Yu_Zhao1, ~Longyue_Wang3, ~Tianqi_Shi1, ~Weihua_Luo2, ~Kaifu_Zhang2, ~Jinsong_Su1 | 20250919 | https://openreview.net/forum?id=Zz8ikW4uWG | Zz8ikW4uWG | @inproceedings{
zhang2026a,
title={A State-Transition Framework for Efficient {LLM} Reasoning},
author={Liang Zhang and Yu Zhao and Longyue Wang and Tianqi Shi and Weihua Luo and Kaifu Zhang and Jinsong Su},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openr... | OpenReview/ICLR/figures/2026/accept_poster/Zz8ikW4uWG/Figure4.png | 4 | Figure 4: (a) shows the computational and memory efficiency of our model and the base model. (b) and (c) present our model’s performance with different values of hyper-parameters β and αmax, respectively. These experiments are conducted on Qwen2.5-1.5B. | <paragraph_1>Analysis of Computational and Memory Costs. We conduct experiments to further compare the computational and memory efficiency of our model and the base model across varying CoT lengths. The experimental results are presented in Figure 4(a). Although our model exhibits similar reasoning efficiency to the ba... | diagram | 0.868907 | |
OpenReview | ICLR | 2,026 | STITCH: Simultaneous Thinking and Talking with Chunked Reasoning for Spoken Language Models | Spoken Language Models (SLMs) are designed to take speech inputs and produce
spoken responses. However, current SLMs lack the ability to perform an internal,
unspoken thinking process before responding. In contrast, humans typically engage
in complex mental reasoning internally, enabling them to communicate ideas clear... | spoken language model, reasoning, chain-of-thought | applications to computer vision, audio, language, and other modalities | [
6,
4,
6,
4
] | Accept (Poster) | Cheng-Han Chiang, Xiaofei Wang, Linjie Li, Chung-Ching Lin, Kevin Lin, Shujie LIU, Zhendong Wang, Zhengyuan Yang, Hung-yi Lee, Lijuan Wang | ~Cheng-Han_Chiang1, ~Xiaofei_Wang9, ~Linjie_Li1, ~Chung-Ching_Lin2, ~Kevin_Lin3, ~Shujie_LIU1, ~Zhendong_Wang1, ~Zhengyuan_Yang1, ~Hung-yi_Lee2, ~Lijuan_Wang1 | 20250915 | https://openreview.net/forum?id=5Z1eMhCeTb | 5Z1eMhCeTb | @inproceedings{
chiang2026stitch,
title={{STITCH}: Simultaneous Thinking and Talking with Chunked Reasoning for Spoken Language Models},
author={Cheng-Han Chiang and Xiaofei Wang and Linjie Li and Chung-Ching Lin and Kevin Lin and Shujie LIU and Zhendong Wang and Zhengyuan Yang and Hung-yi Lee and Lijuan Wang},
booktit... | OpenReview/ICLR/figures/2026/accept_poster/5Z1eMhCeTb/Figure2.png | 2 | Figure 2: Different generation method explored in this paper. The arrow represents the timeline for the SLM to generate the tokens; this timeline should not be confused with the timeline that the end user receives the audio, i.e., the upper timeline in Figure 1. We plot tokens of the same type in a chunk using the same... | <paragraph_1>In the interleaved decoding paradigm, the SLM backbone model generates a chunk of text tokens and a chunk of speech tokens alternately. The text tokens serve as guidance for future speech tokens by transcribing what the speech token will say. For example, GLM-4-Voice (Zeng et al., 2024) interleaves between... | diagram | 0.959533 | |
OpenReview | ICLR | 2,026 | Seeing Across Views: Benchmarking Spatial Reasoning of Vision-Language Models in Robotic Scenes | Vision-language models (VLMs) are essential to Embodied AI, enabling robots to perceive, reason, and act in complex environments. They also serve as the foundation for the recent Vision-Language-Action (VLA) models. Yet, most evaluations of VLMs focus on single-view settings, leaving their ability to integrate multi-vi... | spatial understanding, benchmark, multi-view, vlm, robotics | datasets and benchmarks | MV-RoboBench evaluates whether vision–language models can integrate multi-view images for precise robotic perception and decision-making, revealing major gaps compared to human performance. | [
8,
6,
6,
6
] | Accept (Poster) | ZhiYuan Feng, Zhaolu Kang, Qijie Wang, Zhiying Du, Jiongrui Yan, Shi Shubin, Chengbo Yuan, Huizhi Liang, Yu Deng, Qixiu Li, Rushuai Yang, Ruichuan An, Leqi Zheng, Weijie Wang, Shawn Chen, Sicheng Xu, Yaobo Liang, Jiaolong Yang, Baining Guo | ~ZhiYuan_Feng1, ~Zhaolu_Kang2, ~Qijie_Wang1, ~Zhiying_Du1, ~Jiongrui_Yan1, ~Shi_Shubin3, ~Chengbo_Yuan2, ~Huizhi_Liang1, ~Yu_Deng2, ~Qixiu_Li1, ~Rushuai_Yang1, ~Ruichuan_An1, ~Leqi_Zheng1, ~Weijie_Wang2, ~Shawn_Chen1, ~Sicheng_Xu1, ~Yaobo_Liang1, ~Jiaolong_Yang3, ~Baining_Guo1 | 20250913 | https://openreview.net/forum?id=jXDZJAfRZB | jXDZJAfRZB | @inproceedings{
feng2026seeing,
title={Seeing Across Views: Benchmarking Spatial Reasoning of Vision-Language Models in Robotic Scenes},
author={ZhiYuan Feng and Zhaolu Kang and Qijie Wang and Zhiying Du and Jiongrui Yan and Shi Shubin and Chengbo Yuan and Huizhi Liang and Yu Deng and Qixiu Li and Rushuai Yang and Ruic... | OpenReview/ICLR/figures/2026/accept_poster/jXDZJAfRZB/Figure12.png | 12 | Figure 12: Illustration of the righthanded coordinate system defined relative to each camera. | <paragraph_1>Directional convention. In summary, +z = upward, −z = downward; +y = forward, −y = backward; +x = right, −x = left. Figure 12 provides an illustration of this definition.</paragraph_1> | diagram | 0.955413 |
OpenReview | ICLR | 2,026 | R-Horizon: How Far Can Your Large Reasoning Model Really Go in Breadth and Depth? | Recent trends in test-time scaling for reasoning models (e.g., OpenAI o1, DeepSeek-R1) have led to remarkable improvements through long Chain-of-Thought (CoT). However, existing benchmarks mainly focus on immediate, single-horizon tasks, failing to adequately evaluate models’ ability to understand and respond to comple... | Large Reasoning Models, Long Horizon Reasoning | foundation or frontier models, including LLMs | A scalable, controllable, and low-cost paradigm for enhancing and evaluating the long-horizon reasoning capabilities of LRMs | [
6,
6,
6,
6
] | Accept (Poster) | Yi Lu, Jianing Wang, Linsen Guo, Wei He, Hongyin Tang, Tao Gui, Xuanjing Huang, Xuezhi Cao, Wei Wang, Xunliang Cai | ~Yi_Lu7, ~Jianing_Wang4, ~Linsen_Guo2, ~Wei_He14, ~Hongyin_Tang1, ~Tao_Gui1, ~Xuanjing_Huang1, ~Xuezhi_Cao1, ~Wei_Wang41, ~Xunliang_Cai1 | 20250916 | https://openreview.net/forum?id=rRB1bYErbL | rRB1bYErbL | @inproceedings{
lu2026rhorizon,
title={R-Horizon: How Far Can Your Large Reasoning Model Really Go in Breadth and Depth?},
author={Yi Lu and Jianing Wang and Linsen Guo and Wei He and Hongyin Tang and Tao Gui and Xuanjing Huang and Xuezhi Cao and Wei Wang and Xunliang Cai},
booktitle={The Fourteenth International Confe... | OpenReview/ICLR/figures/2026/accept_poster/rRB1bYErbL/Figure2.png | 2 | Figure 2: The R-HORIZON data composition pipeline is illustrated in (a)-(c). We leverage RHORIZON to construct a comprehensive long-horizon reasoning evaluation benchmark spanning 6 tasks and generate multi-horizon training data for long-horizon reinforcement learning. | <paragraph_1>We propose R-HORIZON, a method designed to stimulate long-horizon reasoning behaviors in LRMs via query composition. As illustrated in Figure 2, R-HORIZON supports the concatenation of three types of expanded questions and can be employed in both the training and evaluation stages to enhance and evaluate t... | diagram | 0.95814 |
OpenReview | ICLR | 2,026 | IGC-Net for conditional average potential outcome estimation over time | Estimating potential outcomes for treatments over time based on observational data is important for personalized decision-making in medicine. However, many existing methods for this task fail to properly adjust for time-varying confounding and thus yield biased estimates. There are only a few neural methods with proper... | causal inference, potential outcomes, treatment effects, healthcare | causal reasoning | We develop a novel neural method that performs G-computation in an iterative end-to-end training algorithm for conditional average potential outcome estimation over time. | [
8,
6,
2,
4,
4
] | Accept (Poster) | Konstantin Hess, Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel | ~Konstantin_Hess1, ~Dennis_Frauen1, ~Valentyn_Melnychuk1, ~Stefan_Feuerriegel1 | 20250916 | https://openreview.net/forum?id=ZmhpqpKzAT | ZmhpqpKzAT | @inproceedings{
hess2026igcnet,
title={{IGC}-Net for conditional average potential outcome estimation over time},
author={Konstantin Hess and Dennis Frauen and Valentyn Melnychuk and Stefan Feuerriegel},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openrevie... | OpenReview/ICLR/figures/2026/accept_poster/ZmhpqpKzAT/Figure1.png | 1 | Figure 1: Iterative G-computation network. Neural end-toend architecture and training of our iterative G-computation network. | <paragraph_1>Our IGC-Net consists of two key components (see Figure 1): (i) a neural backbone zϕ(·), which can be, for example, be an LSTM or a transformer, and (ii) several G-computation heads {gϕ δ (·)}τ−1 δ=0, where ϕ denote the trainable weights. First, the neural backbone encodes the entire observed history. Then,... | diagram | 0.992686 |
OpenReview | ICLR | 2,026 | **TandemFoilSet**: Datasets for Flow Field Prediction of Tandem-Airfoil Through the Reuse of Single Airfoils | Accurate simulation of flow fields around tandem geometries is critical for engineering design but remains computationally intensive. Existing machine learning approaches typically focus on simpler cases and lack evaluation on multi-body configurations. To support research in this area, we present **TandemFoilSet**: fi... | Physics-informed Graph Neural Network; Tandem-Airfoil; Flow Field Prediction; CFD; Aerodynamics; | datasets and benchmarks | We introduce TandemFoilSet, a paired set of 5 tandem-airfoil + 4 single-airfoil CFD datasets (8,104 simulations total) and baseline benchmarks to enable scalable ML flow-field prediction for tandem-airfoil interactions. | [
2,
6,
6,
4
] | Accept (Poster) | Wei Xian Lim, Loh Sher En Jessica, Zenong Li, Thant Zin Oo, Wai Lee Chan, Adams Wai-Kin Kong | ~Wei_Xian_Lim2, ~Loh_Sher_En_Jessica1, ~Zenong_Li1, ~Thant_Zin_Oo1, ~Wai_Lee_Chan1, ~Adams_Wai-Kin_Kong1 | 20250918 | https://openreview.net/forum?id=4Z0P4Nbosn | 4Z0P4Nbosn | @inproceedings{
lim2026tandemfoilset,
title={**TandemFoilSet**: Datasets for Flow Field Prediction of Tandem-Airfoil Through the Reuse of Single Airfoils},
author={Wei Xian Lim and Loh Sher En Jessica and Zenong Li and Thant Zin Oo and Wai Lee Chan and Adams Wai-Kin Kong},
booktitle={The Fourteenth International Confer... | OpenReview/ICLR/figures/2026/accept_poster/4Z0P4Nbosn/Figure16.png | 16 | Figure 16: Determining obstruction of a boundary point from the reference point in a (a) single-object case and (b) double-object case. Note how a boundary point that is unobstructed in the first case may be obstructed by another object in the second case. | <paragraph_1>As mentions previously, the DID was estimated numerically following the procedure outlined in Algorithm 1. Although extending the theoretical definition of DID to multiple geometries is conceptually straightforward, the numerical calculations grow significantly more complex with each additional object. The... | diagram | 0.991554 |
OpenReview | ICLR | 2,026 | Understanding and Improving Length Generalization in Hierarchical Sparse Attention Models | Effectively processing long contexts is a critical challenge for language models. While standard Transformers are limited by quadratic complexity and poor length extrapolation, alternative architectures like sliding window attention and state space models sacrifice the ability to effectively utilize the full context du... | long-context modeling, length generalization, length extrapolation, sparse attention, language modeling | unsupervised, self-supervised, semi-supervised, and supervised representation learning | We demonstrate that extreme length generalization in hierarchical sparse attention is enabled by the interplay of an expressive chunking, a stable bypassing residual path, and enforced retrieval sparsity. | [
4,
6,
4,
8
] | Accept (Poster) | Jiaqi Leng, Xiang Hu, Junxiong Wang, Jianguo Li, Wei Wu, Yucheng Lu | ~Jiaqi_Leng3, ~Xiang_Hu2, ~Junxiong_Wang1, ~Jianguo_Li2, ~Wei_Wu1, ~Yucheng_Lu1 | 20250912 | https://openreview.net/forum?id=iHqdSQk6qc | iHqdSQk6qc | @inproceedings{
leng2026understanding,
title={Understanding and Improving Length Generalization in Hierarchical Sparse Attention Models},
author={Jiaqi Leng and Xiang Hu and Junxiong Wang and Jianguo Li and Wei Wu and Yucheng Lu},
booktitle={The Fourteenth International Conference on Learning Representations},
year={20... | OpenReview/ICLR/figures/2026/accept_poster/iHqdSQk6qc/Figure2.png | 2 | Figure 2: Design of Encoder: (a): Encoder w/o CLS (b): Encoder with a learnable CLS token. | <paragraph_1>The different architectural configurations we investigate, summarized in Table 1, can be expressed as joint definitions of (f, g). In the “w/ CLS” variant, we prepend a learnable token, xCLS, to the input chunk H[i], as shown in Fig. 2. The Encoder processes this combined sequence, and its output correspon... | diagram | 0.911093 |
OpenReview | ICLR | 2,026 | Omni-Weather: Unified Multimodal Foundation Model for Weather Generation and Understanding | Weather modeling requires both accurate prediction and mechanistic interpretation, yet existing methods treat these goals in isolation, separating generation from understanding. To address this gap, we present Omni-Weather, the first multimodal foundation model that unifies weather generation and understanding within a... | AI for Science, Unified foundation model, Interpretable reasoning | applications to physical sciences (physics, chemistry, biology, etc.) | [
6,
6,
4,
8
] | Accept (Poster) | Zhiwang Zhou, Yuandong Pu, Xuming He, Yidi Liu, Yixin Chen, Junchao Gong, Xiang Zhuang, Wanghan Xu, Qinglong Cao, SHIXIANG TANG, Yihao Liu, Wenlong Zhang, LEI BAI | ~Zhiwang_Zhou1, ~Yuandong_Pu1, ~Xuming_He4, ~Yidi_Liu3, ~Yixin_Chen26, ~Junchao_Gong1, ~Xiang_Zhuang1, ~Wanghan_Xu1, ~Qinglong_Cao1, ~SHIXIANG_TANG1, ~Yihao_Liu1, ~Wenlong_Zhang3, ~LEI_BAI1 | 20250910 | https://openreview.net/forum?id=3WnXsp72v6 | 3WnXsp72v6 | @inproceedings{
zhou2026omniweather,
title={Omni-Weather: Unified Multimodal Foundation Model for Weather Generation and Understanding},
author={Zhiwang Zhou and Yuandong Pu and Xuming He and Yidi Liu and Yixin Chen and Junchao Gong and Xiang Zhuang and Wanghan Xu and Qinglong Cao and SHIXIANG TANG and Yihao Liu and We... | OpenReview/ICLR/figures/2026/accept_poster/3WnXsp72v6/Figure2.png | 2 | Figure 2: Comparison between separated architectures for weather understanding / generation (top) and unified framework with shared self-attention (bottom). | <paragraph_1>Despite these advances, unified architectures remain absent in the weather domain. As shown in Figure 2, existing approaches are divided into two disjoint paradigms: model such as ClimaX Nguyen et al. (2023) and WeatherGFM Zhao et al. (2024) excel at forecasting and downscaling but lack interpretation, whi... | diagram | 0.992263 | |
OpenReview | ICLR | 2,026 | Weight Space Representation Learning on Diverse NeRF Architectures | Neural Radiance Fields (NeRFs) have emerged as a groundbreaking paradigm for representing 3D objects and scenes by encoding shape and appearance information into the weights of a neural network. Recent studies have demonstrated that these weights can be used as input for frameworks designed to address deep learning tas... | weight space learning, representation learning, metanetworks, graph metanetworks, neural fields, neural radiance fields, NeRF, implicit neural representations, INR | unsupervised, self-supervised, semi-supervised, and supervised representation learning | We present the first framework that performs tasks on NeRFs by processing their weights and is able to work on diverse architectures | [
6,
4,
4,
6
] | Accept (Poster) | Francesco Ballerini, Pierluigi Zama Ramirez, Luigi Di Stefano, Samuele Salti | ~Francesco_Ballerini1, ~Pierluigi_Zama_Ramirez1, ~Luigi_Di_Stefano2, ~Samuele_Salti1 | 20250918 | https://openreview.net/forum?id=u90rHXaBve | u90rHXaBve | @inproceedings{
ballerini2026weight,
title={Weight Space Representation Learning on Diverse Ne{RF} Architectures},
author={Francesco Ballerini and Pierluigi Zama Ramirez and Luigi Di Stefano and Samuele Salti},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://op... | OpenReview/ICLR/figures/2026/accept_poster/u90rHXaBve/Figure5.png | 5 | Figure 5: Parameter graph conversion. Top left: parameter graph representation of an MLP, proposed by Lim et al. (2024). Right: parameter graph representation of a tri-plane, proposed by Lim et al. (2024). Dotted edges should be connected to the C channel nodes, but are not fully drawn for better visual clarity. Bottom... | <paragraph_1>The parameter graph conversion of an MLP, a tri-plane, and a multi-resolution hash table is depicted in Fig. 5, with additional details compared to Fig. 2 (left).</paragraph_1> | diagram | 0.883032 |
OpenReview | ICLR | 2,026 | Toward Effective Tool-Integrated Reasoning via Self-Evolved Preference Learning | Tool-Integrated Reasoning (TIR) enables large language models (LLMs) to enhance their internal reasoning ability by integrating external tools. However, models with TIR often exhibit suboptimal behaviors, including insufficient tool calls, excessive tool calls, and overthinking after receiving tool call results. How to... | reasoning model, tool-integrated reasoning, self-evolved training, information entropy | foundation or frontier models, including LLMs | [
4,
6,
8,
6
] | Accept (Poster) | Yifei Chen, Guanting Dong, Zhicheng Dou | ~Yifei_Chen12, ~Guanting_Dong1, ~Zhicheng_Dou1 | 20250916 | https://openreview.net/forum?id=mNeitRAdWV | mNeitRAdWV | @inproceedings{
chen2026toward,
title={Toward Effective Tool-Integrated Reasoning via Self-Evolved Preference Learning},
author={Yifei Chen and Guanting Dong and Zhicheng Dou},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=mNeitRAdWV}
... | OpenReview/ICLR/figures/2026/accept_poster/mNeitRAdWV/Figure3.png | 3 | Figure 3: The overall structure of Tool-Light’s training pipeline. Among them, the Self-Evolved DPO Alignment stage will conduct multiple rounds of training. | <paragraph_1>Overview. We propose Tool-Light, a multi-stage training pipeline aiming to improve the effectiveness of model tool calls. As shown in Figures 2 and 3, Tool-Light consists of two key components: (1) Dataset construction, which includes carefully designed sampling strategies to screen out training data. (2) ... | diagram | 0.939537 | |
OpenReview | ICLR | 2,026 | Lookup multivariate Kolmogorov-Arnold Networks | High-dimensional linear mappings, or linear layers, dominate both the parameter count and the computational cost of most modern deep-learning models. We introduce lookup multivariate Kolmogorov-Arnold Networks (lmKANs), which deliver a substantially better trade-off between capacity and inference cost. Our construction... | KAN, inference efficiency, CUDA kernels | other topics in machine learning (i.e., none of the above) | We propose a fully connected layer that decouples inference efficiency from the number of trainable parameters and empirically find it to be Pareto optimal across a wide range of macro-architectural backbones. | [
6,
2,
6,
6
] | Accept (Poster) | Sergey Pozdnyakov, Philippe Schwaller | ~Sergey_Pozdnyakov1, ~Philippe_Schwaller1 | 20250919 | https://openreview.net/forum?id=XRQVIeBnB0 | XRQVIeBnB0 | @inproceedings{
pozdnyakov2026lookup,
title={Lookup multivariate Kolmogorov-Arnold Networks},
author={Sergey Pozdnyakov and Philippe Schwaller},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=XRQVIeBnB0}
} | OpenReview/ICLR/figures/2026/accept_poster/XRQVIeBnB0/Figure6.png | 6 | Figure 6: A methane configuration | <paragraph_1>Having demonstrated that lmKANs are Pareto-optimal when approximating a general function, we proceed to benchmark their efficiency on real data. We chose the tabular-like dataset of randomly displaced methane configurations for the comparison, as it is particularly suitable for this purpose (see Appendix G... | diagram | 0.866739 |
OpenReview | ICLR | 2,026 | Automata Learning and Identification of the Support of Language Models | We study the learnability of languages in the *Next Symbol Prediction* (NSP) setting, where a learner receives only positive examples from a language together with, for every prefix, (i) whether the prefix itself is in the language and (ii) which next symbols can lead to an accepting string. This setting has been used ... | automata learning, regular languages, learning theory, DFA extraction, language models | learning theory | [
8,
6,
6,
8
] | Accept (Poster) | Satwik Bhattamishra, Michael Hahn, Varun Kanade | ~Satwik_Bhattamishra1, ~Michael_Hahn1, ~Varun_Kanade1 | 20250919 | https://openreview.net/forum?id=L8SMNWsxfK | L8SMNWsxfK | @inproceedings{
bhattamishra2026automata,
title={Automata Learning and Identification of the Support of Language Models},
author={Satwik Bhattamishra and Michael Hahn and Varun Kanade},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=L8S... | OpenReview/ICLR/figures/2026/accept_poster/L8SMNWsxfK/Figure7.png | 7 | Figure 7: DFA with 28 states extracted by L⋆ nsp from Transformer trained on Tomita-5. See App. H.2 for more details. | <paragraph_1>Identifying erroneous examples. When the learned DFA ˆA is not equivalent to the target DFA A⋆, we construct the product DFA B which recognizes the strings in the symmetric difference of the two languages L(B) = L( ˆA)△L(A⋆). We use a BFS-like approach to identify several erroneous examples for the languag... | diagram | 0.92614 | |
OpenReview | ICLR | 2,026 | Nef-Net v2: Adapting Electrocardio Panorama in the wild | Conventional multi-lead electrocardiogram (ECG) systems capture cardiac signals
from a fixed set of anatomical viewpoints defined by lead placement. However, cer-
tain cardiac conditions (e.g., Brugada syndrome) require additional, non-standard
viewpoints to reveal diagnostically critical patterns that may be absent in... | ECG representation, Cardiac Diagnosis | applications to physical sciences (physics, chemistry, biology, etc.) | An enhanced variant of Nef-Net to generate panoramic ECG views, including previously unseen views. | [
6,
2,
6
] | Accept (Poster) | Zehui Zhan, Yaojun Hu, Jiajing Zhang, Wanchen Lian, Wanqing Wu, Jintai Chen | ~Zehui_Zhan1, ~Yaojun_Hu2, ~Jiajing_Zhang1, ~Wanchen_Lian1, ~Wanqing_Wu1, ~Jintai_Chen1 | 20250917 | https://openreview.net/forum?id=JzZhhhxniR | JzZhhhxniR | @inproceedings{
zhan2026nefnet,
title={Nef-Net v2: Adapting Electrocardio Panorama in the wild},
author={Zehui Zhan and Yaojun Hu and Jiajing Zhang and Wanchen Lian and Wanqing Wu and Jintai Chen},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/... | OpenReview/ICLR/figures/2026/accept_poster/JzZhhhxniR/Figure2.png | 2 | Figure 2: NEF-NET V2 architecture for Electrocardio Panorama synthesis (illustrated for a 3-input to 2-query view synthesis task as example). The NEF-NET V2 first employs a View Encoder to extract features from the Recorded ECG that are relevant to the Queried ECG. These extracted features are then fused using a Geomet... | <paragraph_1>The key idea of NEF-NET V2 is to formulate ECG view synthesis as a direct view-to-view transformation problem. This is a pairwise deterministic mapping: the model converts the observed lead signals into the target lead through a single-step transformation, without modeling any shared geometric prior (e.g.,... | diagram | 0.992609 |
OpenReview | ICLR | 2,026 | Unified Vision–Language Modeling via Concept Space Alignment | We introduce vSONAR, a vision–language embedding space extended from the text-only embedding space SONAR, which supports 200 text languages and 37 speech languages.
To construct vSONAR, we propose a post-hoc alignment pipeline that maps the representations of an existing vision encoder into the SONAR space.
We thorough... | multimodal embedding space, multilingual embedding space | applications to computer vision, audio, language, and other modalities | [
6,
6,
6,
4
] | Accept (Poster) | Yifu QIU, Paul-Ambroise Duquenne, Holger Schwenk | ~Yifu_QIU1, ~Paul-Ambroise_Duquenne1, ~Holger_Schwenk1 | 20250918 | https://openreview.net/forum?id=4LiX5ddGcU | 4LiX5ddGcU | @inproceedings{
qiu2026unified,
title={Unified Vision{\textendash}Language Modeling via Concept Space Alignment},
author={Yifu QIU and Paul-Ambroise Duquenne and Holger Schwenk},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=4LiX5ddGcU... | OpenReview/ICLR/figures/2026/accept_poster/4LiX5ddGcU/Figure1.png | 1 | Figure 1: Left: Illustration of V-SONAR. Right: fine-tuning V-LCM with vision-language instruction tuning. | <paragraph_1>Architecture The architecture of V-SONAR is illustrated in the left panel of Figure 1. Given the input image or video, PERCEPTION ENCODER (PE) will first encode each frame separately. Then, we stack a lightweight projector on top of PE to adapt the encoder’s representations into the SONAR space. The projec... | diagram | 0.931501 | |
OpenReview | ICLR | 2,026 | Not All Clients Are Equal: Collaborative Model Personalization on Heterogeneous Multi-Modal Clients | As AI becomes more personal, e.g., Agentic AI, there is an increasing need for personalizing models for various use cases.
Personalized federated learning (PFL) enables each client to collaboratively leverage other clients' knowledge for better adaptation to the task of interest, without privacy risks. Despite its pote... | Collaborative Learning, Federated Learning, Continual Learning, Multi-modal Learning, Personalization, Distributed Learning | applications to computer vision, audio, language, and other modalities | [
10,
4,
6,
8
] | Accept (Poster) | Minhyuk Seo, Taeheon Kim, Hankook Lee, Jonghyun Choi, Tinne Tuytelaars | ~Minhyuk_Seo1, ~Taeheon_Kim3, ~Hankook_Lee1, ~Jonghyun_Choi1, ~Tinne_Tuytelaars1 | 20250918 | https://openreview.net/forum?id=0g5Dk4Qfh0 | 0g5Dk4Qfh0 | @inproceedings{
seo2026not,
title={Not All Clients Are Equal: Collaborative Model Personalization on Heterogeneous Multi-Modal Clients},
author={Minhyuk Seo and Taeheon Kim and Hankook Lee and Jonghyun Choi and Tinne Tuytelaars},
booktitle={The Fourteenth International Conference on Learning Representations},
year={202... | OpenReview/ICLR/figures/2026/accept_poster/0g5Dk4Qfh0/Figure14.png | 14 | Figure 14: Illustration of blockwise PQ-LoRA. When a model has NB PQ-LoRA modules, each block employs PQ-LoRA at its last layer, while the remaining layers adopt conventional LoRA. Each block contains the same number of layers. | <paragraph_1>To identify layer-wise correspondences between depth-heterogeneous models, we analyze representation alignment using CKA (Kornblith et al., 2019). Specifically, we measure similarity across layers within the Llama-3 family (1B, 3B, 8B) and the Qwen-2.5 family (0.5B, 1.5B, 3B), as illustrated in Fig. 12. As... | diagram | 0.962517 | |
OpenReview | ICLR | 2,026 | FreeKV: Boosting KV Cache Retrieval for Efficient LLM Inference | Large language models (LLMs) have been widely deployed with rapidly expanding context windows to support increasingly demanding applications.
However, long contexts pose significant deployment challenges, primarily due to the KV cache whose size grows proportionally with context length.
While KV cache compression metho... | LLM inference, KV cache | infrastructure, software libraries, hardware, systems, etc. | We propose FreeKV, an algorithm-system co-optimization framework for LLM inference to enhance KV retrieval efficiency while preserving accuracy. | [
8,
2,
6,
6
] | Accept (Poster) | Guangda Liu, Chengwei Li, Zhenyu Ning, Jing Lin, Yiwu Yao, Danning Ke, Minyi Guo, Jieru Zhao | ~Guangda_Liu1, ~Chengwei_Li1, ~Zhenyu_Ning1, ~Jing_Lin6, ~Yiwu_Yao1, ~Danning_Ke1, ~Minyi_Guo1, ~Jieru_Zhao1 | 20250918 | https://openreview.net/forum?id=wXAn7orB1H | wXAn7orB1H | @inproceedings{
liu2026freekv,
title={Free{KV}: Boosting {KV} Cache Retrieval for Efficient {LLM} Inference},
author={Guangda Liu and Chengwei Li and Zhenyu Ning and Jing Lin and Yiwu Yao and Danning Ke and Minyi Guo and Jieru Zhao},
booktitle={The Fourteenth International Conference on Learning Representations},
year=... | OpenReview/ICLR/figures/2026/accept_poster/wXAn7orB1H/Figure5.png | 5 | Figure 5: System overview of FreeKV. | <paragraph_1>The system overview of FreeKV is illustrated in Fig. 5. In the data plane, FreeKV retains the query vectors from the previous step, page summaries and cache for selected KV pages in GPU memory. In CPU memory, FreeKV maintains a complete KV cache pool for offloading KV pages. In the control plane, a control... | diagram | 0.981499 |
OpenReview | ICLR | 2,026 | Fine-Grained Activation Steering: Steering Less, Achieving More | Activation steering has emerged as a cost-effective paradigm for modifying large language model (LLM) behaviors. Existing methods typically intervene at the block level, steering the bundled activations of selected attention heads, feedforward networks, or residual streams. However, we reveal that block-level activatio... | Activation Steering, Large Language Models, Fine-Grained Intervention | foundation or frontier models, including LLMs | Breaking LLM blocks to fine-grained atomic units for intervention: steering less achieves more | [
4,
4,
6
] | Accept (Poster) | Zijian Feng, Tianjiao Li, Zixiao Zhu, Hanzhang Zhou, Junlang Qian, Li Zhang, Chua Jia Jim Deryl, Mak Lee Onn, Gee Wah Ng, Kezhi Mao | ~Zijian_Feng2, ~Tianjiao_Li2, ~Zixiao_Zhu2, ~Hanzhang_Zhou1, ~Junlang_Qian1, ~Li_Zhang70, ~Chua_Jia_Jim_Deryl2, ~Mak_Lee_Onn1, ~Gee_Wah_Ng1, ~Kezhi_Mao1 | 20250918 | https://openreview.net/forum?id=guSVafqhrB | guSVafqhrB | @inproceedings{
feng2026finegrained,
title={Fine-Grained Activation Steering: Steering Less, Achieving More},
author={Zijian Feng and Tianjiao Li and Zixiao Zhu and Hanzhang Zhou and Junlang Qian and Li Zhang and Chua Jia Jim Deryl and Mak Lee Onn and Gee Wah Ng and Kezhi Mao},
booktitle={The Fourteenth International C... | OpenReview/ICLR/figures/2026/accept_poster/guSVafqhrB/Figure1.png | 1 | Figure 1: Comparison of block-level steering (prior work) and AU-level steering (Ours). | <paragraph_1>However, a common practice in existing methods is block-level steering, where a “block” denotes the multi-head attention (MHA), the feed-forward network (FFN), or the layer’s residual stream. As shown in Figure 1 (a), the intervention is vector-level: every dimension of the selected block’s activation is b... | diagram | 0.998495 |
OpenReview | ICLR | 2,026 | Counterfactual Structural Causal Bandits | Causal reasoning lies at the heart of robust and generalizable decision-making, and the *Pearl Causal Hierarchy* provides a formal language for distinguishing between observational ($\mathcal{L}_1$), interventional ($\mathcal{L}_2$), and counterfactual ($\mathcal{L}_3$) levels of reasoning. Existing bandit algorithms t... | causal inference, counterfactual inference, structural causal bandits, causal decision making | causal reasoning | We introduce a counterfactual structural causal bandit (ctf-SCB) framework which expands the agent's feasible action space beyond conventional observational and interventional arms to include a class of realizable counterfactual actions. | [
4,
4,
6,
8
] | Accept (Poster) | Min Woo Park, Sanghack Lee | ~Min_Woo_Park1, ~Sanghack_Lee1 | 20250920 | https://openreview.net/forum?id=gjvTNxVd2f | gjvTNxVd2f | @inproceedings{
park2026counterfactual,
title={Counterfactual Structural Causal Bandits},
author={Min Woo Park and Sanghack Lee},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=gjvTNxVd2f}
} | OpenReview/ICLR/figures/2026/accept_poster/gjvTNxVd2f/Figure10.png | 10 | Figure 10: MUCT and IB are shown in red and blue, respectively; (b, c) non-POMISs; (d, e) POMISs. | <paragraph_1>For example, consider the causal diagram in Fig. 10a. Here, G = G[An(Y )G] holds. An L1 action do(∅) is not a POMIS. To see this, we construct MUCT, initializing T = {Y }, as follows: Since Y has an unobserved confounder with C, we update T = cc(Y )G = {C, Y }, and thereafter add all the descendants of C, ... | diagram | 0.990313 |
OpenReview | ICLR | 2,026 | SpaCE-Eval: A Benchmark for Real-World Multi-Modal Reasoning | Multi-modal Large Language Models (MLLMs) represent a significant advancement in artificial intelligence. Among the growing capabilities exhibited by MLLMs, abilities to understand and reason in real-world environments stand out as particularly vital as a fundamental prerequisite for a wide array of real-world applicat... | Benchmark, Multi-modal Large Language Model, Visual Reasoning, Real World Environments, Evaluation | datasets and benchmarks | [
6,
4,
6,
6
] | Accept (Poster) | Xuyou Yang, Yucheng Zhao, Wenxuan Zhang, Immanuel Koh | ~Xuyou_Yang1, ~Yucheng_Zhao3, ~Wenxuan_Zhang1, ~Immanuel_Koh1 | 20250919 | https://openreview.net/forum?id=VAEkLS9VBr | VAEkLS9VBr | @inproceedings{
yang2026spaceeval,
title={Spa{CE}-Eval: A Benchmark for Real-World Multi-Modal Reasoning},
author={Xuyou Yang and Yucheng Zhao and Wenxuan Zhang and Immanuel Koh},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=VAEkLS9VB... | OpenReview/ICLR/figures/2026/accept_poster/VAEkLS9VBr/Figure9.png | 9 | Figure 9: Example of Spatial Reasoning/Form Transformation. | diagram | 0.873445 | ||
OpenReview | ICLR | 2,026 | GaussianFusion: Unified 3D Gaussian Representation for Multi-Modal Fusion Perception | The bird’s-eye view (BEV) representation enables multi-sensor features to be fused within a unified space, serving as the primary approach for achieving comprehensive multi-task perception. However, the discrete grid representation of BEV leads to significant detail loss and limits feature alignment and cross-modal inf... | Gaussian Representation, BEV Representation, Detection, Occupancy | applications to robotics, autonomy, planning | [
2,
4,
6,
6
] | Accept (Poster) | Xiao Zhao, Chang Liu, Mingxu Zhu, Zheyuan Zhang, Linna Song, Qingliang Luo, Chufan Guo, Kuifeng Su | ~Xiao_Zhao4, ~Chang_Liu67, ~Mingxu_Zhu1, ~Zheyuan_Zhang6, ~Linna_Song1, ~Qingliang_Luo1, ~Chufan_Guo1, ~Kuifeng_Su1 | 20250916 | https://openreview.net/forum?id=7jXxQ9bGoU | 7jXxQ9bGoU | @inproceedings{
zhao2026gaussianfusion,
title={GaussianFusion: Unified 3D Gaussian Representation for Multi-Modal Fusion Perception},
author={Xiao Zhao and Chang Liu and Mingxu Zhu and Zheyuan Zhang and Linna Song and Qingliang Luo and Chufan Guo and Kuifeng Su},
booktitle={The Fourteenth International Conference on Le... | OpenReview/ICLR/figures/2026/accept_poster/7jXxQ9bGoU/Figure1.png | 1 | Figure 1: Comparison of the discrete BEV representation fusion paradigm Liu et al. (2023b) and our proposed continuous Gaussian representation fusion paradigm. B, G, C, L, and F denote BEV, Gaussian, Camera, Lidar, and Fusion. | <paragraph_1>BEV directly discretizes and quantizes data, leading to inevitable information loss. During feature extraction, perception data are projected onto a fixed-resolution BEV grid, which compresses spatial information. This issue becomes particularly severe when the BEV resolution is low, as it directly impacts ... | diagram | 0.993349 | |
OpenReview | ICLR | 2,026 | Beyond Simple Graphs: Neural Multi-Objective Routing on Multigraphs | Learning-based methods for routing have gained significant attention in recent years, both in single-objective and multi-objective contexts. Yet, existing methods are unsuitable for routing on multigraphs, which feature multiple edges with distinct attributes between node pairs, despite their strong relevance in real-w... | Combinatorial Optimization, Reinforcement Learning, Graph-based Machine Learning, Multigraphs, Traveling Salesman Problem, Multi-Objective Optimization | learning on graphs and other geometries & topologies | We introduce two GNN-based models for routing with multiple objectives on multigraphs and asymmetric graphs | [
8,
4,
4
] | Accept (Poster) | Filip Rydin, Attila Lischka, Jiaming Wu, Morteza Haghir Chehreghani, Balazs Kulcsar | ~Filip_Rydin1, ~Attila_Lischka1, ~Jiaming_Wu3, ~Morteza_Haghir_Chehreghani2, ~Balazs_Kulcsar1 | 20250919 | https://openreview.net/forum?id=55laGcPNZZ | 55laGcPNZZ | @inproceedings{
rydin2026beyond,
title={Beyond Simple Graphs: Neural Multi-Objective Routing on Multigraphs},
author={Filip Rydin and Attila Lischka and Jiaming Wu and Morteza Haghir Chehreghani and Balazs Kulcsar},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https... | OpenReview/ICLR/figures/2026/accept_poster/55laGcPNZZ/Figure1.png | 1 | Figure 1: Edge-based GMS and its most important components. | <paragraph_1>We visualize GMS-EB in Figure 1. The encoder, consisting of L GREAT-layers, outputs edge embeddings. Using them, the decoder constructs valid tours autoregressively. Given the instance s and incomplete route π1:t−1 in construction step t, the decoder selects edge πt with probability pθ(λ)(πt | π1:t−1, s). ... | diagram | 0.998319 |
OpenReview | ICLR | 2,026 | Goedel-Prover-V2: Scaling Formal Theorem Proving with Scaffolded Data Synthesis and Self-Correction | Automated theorem proving (ATP) --- the task of generating a proof that passes automated proof verification given a math question in formal language --- is a critical challenge at the intersection of mathematics and Artificial Intelligence (AI). We introduce Goedel-Prover-V2, a family of two language models that establ... | Theorem Proving, Reasoning | foundation or frontier models, including LLMs | [
6,
6,
4,
6
] | Accept (Poster) | Yong Lin, Shange Tang, Bohan Lyu, Ziran Yang, Jui-Hui Chung, Haoyu Zhao, Lai Jiang, Yihan Geng, Jiawei Ge, Jingruo Sun, Jiayun Wu, Jiri Gesi, Ximing Lu, David Acuna, Kaiyu Yang, Hongzhou Lin, Yejin Choi, Danqi Chen, Sanjeev Arora, Chi Jin | ~Yong_Lin2, ~Shange_Tang1, ~Bohan_Lyu1, ~Ziran_Yang1, ~Jui-Hui_Chung1, ~Haoyu_Zhao1, ~Lai_Jiang4, ~Yihan_Geng1, ~Jiawei_Ge3, ~Jingruo_Sun1, ~Jiayun_Wu1, ~Jiri_Gesi1, ~Ximing_Lu1, ~David_Acuna1, ~Kaiyu_Yang1, ~Hongzhou_Lin1, ~Yejin_Choi1, ~Danqi_Chen1, ~Sanjeev_Arora1, ~Chi_Jin1 | 20250916 | https://openreview.net/forum?id=j4C0nALrgK | j4C0nALrgK | @inproceedings{
lin2026goedelproverv,
title={Goedel-Prover-V2: Scaling Formal Theorem Proving with Scaffolded Data Synthesis and Self-Correction},
author={Yong Lin and Shange Tang and Bohan Lyu and Ziran Yang and Jui-Hui Chung and Haoyu Zhao and Lai Jiang and Yihan Geng and Jiawei Ge and Jingruo Sun and Jiayun Wu and J... | OpenReview/ICLR/figures/2026/accept_poster/j4C0nALrgK/Figure3.png | 3 | Figure 3: The overall pipeline of model training. | <paragraph_1>We observe that while DeepSeek-Prover-V2 models are already heavily trained and have lost selfcorrection capabilities, other models like Qwen3 lack the ability to generate formal proofs. To address this trade-off, we use data distilled from DeepSeek-Prover-V2 to cold-start Qwen3, followed by large-scale ge... | diagram | 0.951549 | |
OpenReview | ICLR | 2,026 | Learning Unified Representation of 3D Gaussian Splatting | A well-designed vectorized representation is crucial for the learning systems natively based on 3D Gaussian Splatting. While 3DGS enables efficient and explicit 3D reconstruction, its parameter-based representation remains hard to learn as features, especially for neural-network-based models. Directly feeding raw Gauss... | Representation Learning, 3D Gaussian Splatting | unsupervised, self-supervised, semi-supervised, and supervised representation learning | Proposed a new representation of 3DGS based on submanifold field that is more suitable for learning. | [
2,
4,
8,
8
] | Accept (Poster) | Yuelin Xin, Yuheng Liu, Xiaohui Xie, Xinke Li | ~Yuelin_Xin1, ~Yuheng_Liu1, ~Xiaohui_Xie2, ~Xinke_Li1 | 20250904 | https://openreview.net/forum?id=NvpVtGG6hk | NvpVtGG6hk | @inproceedings{
xin2026learning,
title={Learning Unified Representation of 3D Gaussian Splatting},
author={Yuelin Xin and Yuheng Liu and Xiaohui Xie and Xinke Li},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=NvpVtGG6hk}
} | OpenReview/ICLR/figures/2026/accept_poster/NvpVtGG6hk/Figure6.png | 6 | Figure 6: Setting of a Gaussian Neural Field, we compare between the prediction target SF embedding and raw GS parameters. | <paragraph_1>Gaussian Neural Fields. To validate the potential of our representation for advanced downstream tasks, we introduce the Gaussian Neural Field (GNF). Drawing inspiration from the decoding structures in generative diffusion models (e.g., DiffGS by Zhou et al. (2024b)) and neural compression frameworks (Wu & ... | diagram | 0.973273 |
OpenReview | ICLR | 2,026 | Disentangled representation learning through unsupervised symmetry group discovery | Symmetry-based disentangled representation learning leverages the group structure of environment transformations to uncover the latent factors of variation. Prior approaches to symmetry-based disentanglement have required strong prior knowledge of the symmetry group's structure, or restrictive assumptions about the sub... | Representation learning, Disentanglement, Group Theory | unsupervised, self-supervised, semi-supervised, and supervised representation learning | [
8,
4,
8,
6
] | Accept (Poster) | Barthélémy Dang-Nhu, Louis Annabi, Sylvain ARGENTIERI | ~Barthélémy_Dang-Nhu1, ~Louis_Annabi1, ~Sylvain_ARGENTIERI1 | 20250919 | https://openreview.net/forum?id=I6xjMoLY3j | I6xjMoLY3j | @inproceedings{
dang-nhu2026disentangled,
title={Disentangled representation learning through unsupervised symmetry group discovery},
author={Barth{\'e}l{\'e}my Dang-Nhu and Louis Annabi and Sylvain ARGENTIERI},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://o... | OpenReview/ICLR/figures/2026/accept_poster/I6xjMoLY3j/Figure4.png | 4 | Figure 4: Two isomorphic group actions satisfying Assumption 2. | <paragraph_1>We argue that this assumption alone is not sufficient to recover the correct decomposition. To illustrate this point, consider two distinct environments analogous to Flatland shown Figure 4: (a) a 2 3 cyclic grid i.e. Ga Z{2Z Z{3Z with actions Ga tx u Y ty u and (b) a 6 1 cyclic grid i.e. Gb Z{... | diagram | 0.908796 | |
OpenReview | ICLR | 2,026 | On-the-Fly Adaptation to Quantization: Configuration-Aware LoRA for Efficient Fine-Tuning of Quantized LLMs | As increasingly large pre-trained models are released, deploying them on edge devices for privacy-preserving applications requires effective compression. Recent works combine quantization with the fine-tuning of high-precision LoRA adapters, which can substantially reduce model size while mitigating the accuracy loss f... | Configuration-aware optimization, Pareto-base configuration search, Quantization, Fine-tuning | foundation or frontier models, including LLMs | [
4,
6,
6,
6
] | Accept (Poster) | Rongguang Ye, Ming Tang, Edith C. H. Ngai | ~Rongguang_Ye1, ~Ming_Tang5, ~Edith_C._H._Ngai1 | 20250916 | https://openreview.net/forum?id=9OUg0nJE72 | 9OUg0nJE72 | @inproceedings{
ye2026onthefly,
title={On-the-Fly Adaptation to Quantization: Configuration-Aware Lo{RA} for Efficient Fine-Tuning of Quantized {LLM}s},
author={Rongguang Ye and Ming Tang and Edith C. H. Ngai},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://op... | OpenReview/ICLR/figures/2026/accept_poster/9OUg0nJE72/Figure3.png | 3 | Figure 3: Illustration of configuration-aware LoRA adapters with parallel adjustment. The configurationaware model θ generates adjustment matrices I+Uθ(Ci) from the quantization configuration Ci in parallel, where I denotes the identity matrix. | <paragraph_1>Motivated by this observation, we introduce a configuration-aware model θ : R|Qi| →Rr×r, which maps a layer-level configuration vector Qi to a lightweight adjustment matrix Uθ(Qi) ∈Rr×r. As shown in Fig. 3, each layer’s low-rank matrix L2,i is reparameterized as (I + Uθ(Qi))L2,i, where I is the identity ma... | diagram | 0.998697 | |
OpenReview | ICLR | 2,026 | FHE-Coder: Evaluating LLM Agents for secure Fully Homomorphic Encryption Code Generation | Fully Homomorphic Encryption over the Torus (TFHE) is a cornerstone of confidential computing, yet its adoption is severely limited by a steep learning curve requiring specialized cryptographic expertise. To bridge this skills gap, we investigate the potential of Large Language Model (LLM) agents to automate the genera... | Large Language Models, Agents, Code generation, Fully Homomorphic Encryption, Retrieval Augmented Generation | alignment, fairness, safety, privacy, and societal considerations | We built a three-phase agentic framework that enables Large Language Models to automatically generate secure and functional TFHE code, bridging the expertise gap that currently limits the adoption of privacy-preserving computation. | [
6,
4,
6
] | Accept (Poster) | Mayank Kumar, Jiaqi Xue, Mengxin Zheng, Qian Lou | ~Mayank_Kumar8, ~Jiaqi_Xue1, ~Mengxin_Zheng1, ~Qian_Lou1 | 20250919 | https://openreview.net/forum?id=4F1py5vQXm | 4F1py5vQXm | @inproceedings{
kumar2026fhecoder,
title={{FHE}-Coder: Evaluating {LLM} Agents for secure Fully Homomorphic Encryption Code Generation},
author={Mayank Kumar and Jiaqi Xue and Mengxin Zheng and Qian Lou},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openrevi... | OpenReview/ICLR/figures/2026/accept_poster/4F1py5vQXm/Figure4.png | 4 | Figure 4: An offline, human-in-the-loop process creates a dictionary mapping expert-enriched docstrings to code snippets from the TFHE documentation. | <paragraph_1>Therefore, to mitigate each of these issues, we introduce the novel agentic code generation workflow and evaluation framework as shown in Fig. 2. Our workflow is composed of three key components designed to address these specific challenges. First, the FHE Prompt Formalizer (Fig. 3) corrects structural and... | diagram | 0.926502 |
OpenReview | ICLR | 2,026 | PALC: Preference Alignment via Logit Calibration | Aligning Large Language Models with human preferences typically requires computationally intensive training or complex reward architectures. We introduce PALC (Preference Alignment via Logit Calibration), a parameter-efficient framework that achieves test-time alignment through a novel intervention strategy: direct cal... | AI alignment, Representation Editing | alignment, fairness, safety, privacy, and societal considerations | PALC: preference alignment via logit calibration. Learns compact calibrations for frozen LLMs, achieving strong alignment without external rewards or fine-tuning. Outperforms most test-time methods with minimal latency. | [
6,
6,
6,
4
] | Accept (Poster) | SANGHYUN LEE, Hoh Peter In | ~SANGHYUN_LEE4, ~Hoh_Peter_In1 | 20250920 | https://openreview.net/forum?id=0cmuYj3WeG | 0cmuYj3WeG | @inproceedings{
lee2026palc,
title={{PALC}: Preference Alignment via Logit Calibration},
author={SANGHYUN LEE and Hoh Peter In},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=0cmuYj3WeG}
} | OpenReview/ICLR/figures/2026/accept_poster/0cmuYj3WeG/Figure1.png | 1 | Figure 1: Overview of the PALC framework. Unlike conventional representation steering methods that intervene in entangled hidden spaces, PALC treats the base model’s hidden states ht strictly as a read-only context. A lightweight Calibration Module (θ) extracts essential preference signals through a bottleneck architec... | <paragraph_1>We examine how the scaling factor γ affects PALC’s performance. Figure 3 shows results for five values: γ ∈{0.5, 1.0, 3.0, 5.0, 10.0}.</paragraph_1> | diagram | 0.942897 |
OpenReview | ICLR | 2,026 | Neural Predictor-Corrector: Solving Homotopy Problems with Reinforcement Learning | The Homotopy paradigm, a general principle for solving challenging problems, appears across diverse domains such as robust optimization, global optimization, polynomial root-finding, and sampling. Practical solvers for these problems typically follow a predictor-corrector (PC) structure, but rely on hand-crafted heuris... | Homotopy System, Graduated optimization, Reinforcement Learning, Polynomial Equitions System, Gaussian Homotopy, Sampling | applications to computer vision, audio, language, and other modalities | [
6,
6,
4
] | Accept (Poster) | Jiayao Mai, Bangyan Liao, Zhenjun Zhao, Yingping Zeng, Haoang Li, Javier Civera, Tailin Wu, Yi Zhou, Peidong Liu | ~Jiayao_Mai3, ~Bangyan_Liao1, ~Zhenjun_Zhao1, ~Yingping_Zeng1, ~Haoang_Li1, ~Javier_Civera1, ~Tailin_Wu1, ~Yi_Zhou27, ~Peidong_Liu3 | 20250905 | https://openreview.net/forum?id=x6iodYWNty | x6iodYWNty | @inproceedings{
mai2026neural,
title={Neural Predictor-Corrector: Solving Homotopy Problems with Reinforcement Learning},
author={Jiayao Mai and Bangyan Liao and Zhenjun Zhao and Yingping Zeng and Haoang Li and Javier Civera and Tailin Wu and Yi Zhou and Peidong Liu},
booktitle={The Fourteenth International Conference ... | OpenReview/ICLR/figures/2026/accept_poster/x6iodYWNty/Figure2.png | 2 | Figure 2: Illustration of the Predictor-Corrector algorithm. Predictor proposes the next level and provides an initial solution estimate, while Corrector iteratively refines this estimate to project it back onto the solution trajectory. Orange curve denotes the implicit solution trajectory, as in Fig. 1. | <paragraph_1>While the homotopy paradigm specifies the abstract principle, an effective algorithm is needed to trace the implicit solution trajectory in practice. The PC method (Allgower & Georg, 2012) provides such a concrete algorithmic framework. As shown in Fig. 2, PC decomposes trajectory tracking into two complem... | diagram | 0.881063 | |
OpenReview | ICLR | 2,026 | CLUE: Conflict-guided Localization for LLM Unlearning Framework | The LLM unlearning aims to eliminate the influence of undesirable data without affecting causally unrelated information.
This process typically involves using a **forget set** to remove target information, alongside a **retain set** to maintain non-target capabilities. While recent localization-based methods demonstrat... | LLM unlearning, circuit discovery, conjunctive normal form, interpretability | foundation or frontier models, including LLMs | We use circuit discovery and CNF solving to design the localization for forget neurons and retain neurons in the LLM unlearning task. | [
6,
6,
4,
2
] | Accept (Poster) | Hang Chen, Jiaying Zhu, Xinyu Yang, Wenya Wang | ~Hang_Chen3, ~Jiaying_Zhu5, ~Xinyu_Yang2, ~Wenya_Wang1 | 20250901 | https://openreview.net/forum?id=jtRYvazBWv | jtRYvazBWv | @inproceedings{
chen2026clue,
title={{CLUE}: Conflict-guided Localization for {LLM} Unlearning Framework},
author={Hang Chen and Jiaying Zhu and Xinyu Yang and Wenya Wang},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=jtRYvazBWv}
} | OpenReview/ICLR/figures/2026/accept_poster/jtRYvazBWv/Figure2.png | 2 | Figure 2: Overview from datasets to localization. | <paragraph_1>In this section, we provide a three-step framework of how circuit discovery ultimately enables precise localization. An overview of our localization procedure is shown in Figure 2. Specifically,</paragraph_1> | diagram | 0.850337 |
OpenReview | ICLR | 2,026 | Latent Geometry-Driven Network Automata for Complex Network Dismantling | Complex networks model the structure and function of critical technological, biological, and communication systems. Network dismantling, the targeted removal of nodes to fragment a network, is essential for analyzing and improving system robustness. Existing dismantling methods suffer from key limitations: they depend ... | network robustness, network dismantling, network geometry, network science, complex systems, network automata, graphs, network topology | learning on graphs and other geometries & topologies | Latent Geometry-Driven Network Automata dismantles networks by estimating effective link distances on the latent manifold via local rules, outperforming all existing methods on 1,475 real-world networks and runs efficiently on large systems via GPU. | [
4,
2,
6,
6
] | Accept (Poster) | Thomas Adler, Marco Grassia, Ziheng Liao, Giuseppe Mangioni, Carlo Vittorio Cannistraci | ~Thomas_Adler2, ~Marco_Grassia1, ~Ziheng_Liao1, ~Giuseppe_Mangioni1, ~Carlo_Vittorio_Cannistraci1 | 20250918 | https://openreview.net/forum?id=yz29QCGVzC | yz29QCGVzC | @inproceedings{
adler2026latent,
title={Latent Geometry-Driven Network Automata for Complex Network Dismantling},
author={Thomas Adler and Marco Grassia and Ziheng Liao and Giuseppe Mangioni and Carlo Vittorio Cannistraci},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
ur... | OpenReview/ICLR/figures/2026/accept_poster/yz29QCGVzC/Figure1.png | 1 | Figure 1: Overview of the LGD Network Automata framework. A: Begin with an unweighted and undirected network. B: Estimate latent geometry by assigning a weight νij to each edge between nodes i and j using local latent geometry estimators. C: Construct a dissimilarity-weighted network based on these weights. D: Compute ... | <paragraph_1>We introduce the Latent Geometry-Driven Network Automata (LGD-NA) framework. LGD-NA adopts a parameter-free network automaton rule, such as RA2, to estimate latent geometric linked node pairwise distances and to assign edge weights based on these geometric distances. Then, it computes for each node its net... | diagram | 0.976884 |
OpenReview | ICLR | 2,026 | Accelerated co-design of robots through morphological pretraining | The co-design of robot morphology and neural control typically requires using reinforcement learning to approximate a unique control policy gradient for each body plan, demanding massive amounts of training data to measure the performance of each design. Here we show that a universal, morphology-agnostic controller can... | robot co-design, universal control, differentiable simulation, embodied intelligence | applications to robotics, autonomy, planning | [
2,
6,
6
] | Accept (Poster) | Luke Strgar, Sam Kriegman | ~Luke_Strgar1, ~Sam_Kriegman1 | 20250919 | https://openreview.net/forum?id=WVliGyFwZv | WVliGyFwZv | @inproceedings{
strgar2026accelerated,
title={Accelerated co-design of robots through morphological pretraining},
author={Luke Strgar and Sam Kriegman},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=WVliGyFwZv}
} | OpenReview/ICLR/figures/2026/accept_poster/WVliGyFwZv/Figure2.png | 2 | Figure 2: Overview of the proposed method. End-to-end differentiable policy training across tens of millions of morphologically distinct robots—morphological pretraining—produces a universal controller, which was kept frozen throughout zero-shot evolution and finetuned for each generation of few-shot evolution. | <paragraph_1>Inspired by the remarkable success of large-scale pretrained models in computer vision and natural language processing, we here pretrain a universal controller across millions of complex body plans using gradient information from differentiable simulation, averaging gradients across variations in the robot... | diagram | 0.924178 | |
OpenReview | ICLR | 2,026 | Automatic and Structure-Aware Sparsification of Hybrid Neural ODEs with Application to Glucose Prediction | Hybrid neural ordinary differential equations (neural ODEs) integrate mechanistic models with neural ODEs, offering strong inductive bias and flexibility, and are particularly advantageous in data-scarce healthcare settings. However, excessive latent states and interactions from mechanistic models can lead to training ... | Predictive Sparsity, Hybrid Neural ODE, Group LASSO, Glucose Prediction | applications to physical sciences (physics, chemistry, biology, etc.) | [
4,
6,
4,
8
] | Accept (Poster) | Bob Junyi Zou, Lu Tian | ~Bob_Junyi_Zou1, ~Lu_Tian4 | 20250918 | https://openreview.net/forum?id=QBzFrjEF59 | QBzFrjEF59 | @inproceedings{
zou2026automatic,
title={Automatic and Structure-Aware Sparsification of Hybrid Neural {ODE}s with Application to Glucose Prediction},
author={Bob Junyi Zou and Lu Tian},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=QB... | OpenReview/ICLR/figures/2026/accept_poster/QBzFrjEF59/Figure5.png | 5 | Figure 5: An illustration of the mechanistic vs true graphs used in the synthetic experiments | <paragraph_1>In figure 5, we provide an illustration of the mechanistic graph used in the synthetic experiments.</paragraph_1> | diagram | 0.92587 | |
OpenReview | ICLR | 2,026 | Tractability via Low Dimensionality: The Parameterized Complexity of Training Quantized Neural Networks | The training of neural networks has been extensively studied from both algorithmic and complexity-theoretic perspectives, yet recent results in this direction almost exclusively concern real-valued networks. In contrast, advances in machine learning practice highlight the benefits of quantization, where network paramet... | treewidth, parameterized complexity, quantized neural networks, ReLU networks | learning theory | We study the classical and parameterized complexity of training quantized neural networks and obtain new upper as well as lower bounds for the problem. | [
6,
8,
6
] | Accept (Poster) | Robert Ganian, Frank Sommer, Manuel Sorge | ~Robert_Ganian1, ~Frank_Sommer1, ~Manuel_Sorge1 | 20250918 | https://openreview.net/forum?id=BAQNrsr987 | BAQNrsr987 | @inproceedings{
ganian2026tractability,
title={Tractability via Low Dimensionality: The Parameterized Complexity of Training Quantized Neural Networks},
author={Robert Ganian and Frank Sommer and Manuel Sorge},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://op... | OpenReview/ICLR/figures/2026/accept_poster/BAQNrsr987/Figure4.png | 4 | Figure 4: An illustration of the reduction behind Theorem 3 for the universe U = [6] and the set family F with sets S1 = {1, 4, 5}, S2 = {2, 3}, S3 = {1, 6}, S4 = {2, 5}, S5 = {3, 5}, S6 = {6} and k = 3 and with a hitting set S = {2, 5, 6}. In the solution corresponding to S, inputs p1, p2 and p3 are associated with el... | <paragraph_1>We construct an equivalent instance I of 2-QNNT as follows; see Figure 4 for an illustration. Description of architecture G. We create two input neurons z1 and z2. For each of the two literals</paragraph_1>
<paragraph_2>Construction. We construct an instance I of 2-QNNT as follows. For an illustration, see... | diagram | 0.90793 |
OpenReview | ICLR | 2,026 | Constrained Decoding of Diffusion LLMs with Context-Free Grammars | Large language models (LLMs) have shown promising performance across diverse domains. Many practical applications of LLMs, such as code completion and structured data extraction, require adherence to syntactic constraints specified by a formal language. Yet, due to their probabilistic nature, LLM output is not guarante... | diffusion llm, constrained decoding, llm, code generation, json, multi-region infilling, fill in the middle, code synthesis | generative models | We reduce constrained decoding for generalized code generation paradigms to an operation on formal languages, enabling constrained decoding for infilling and diffusion LLMs. | [
4,
8,
6,
4
] | Accept (Poster) | Niels Mündler, Jasper Dekoninck, Martin Vechev | ~Niels_Mündler1, ~Jasper_Dekoninck1, ~Martin_Vechev1 | 20250916 | https://openreview.net/forum?id=7Sph4KyeYO | 7Sph4KyeYO | @inproceedings{
mundler2026constrained,
title={Constrained Decoding of Diffusion {LLM}s with Context-Free Grammars},
author={Niels M{\"u}ndler and Jasper Dekoninck and Martin Vechev},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=7Sph4... | OpenReview/ICLR/figures/2026/accept_poster/7Sph4KyeYO/Figure3.png | 3 | Figure 3: Examples of Figures 1 and 4 processed during our method. (a) The grammar is first normalized into C2F+ε, and (b) the NFA is transformed into a minimal DFA. (c) To determine | <paragraph_1>Constructing the regular language The language Cx of all possible completions of x = x1 . . . xn contains all words that start with x1, end with xn, and contain the strings xi (1 ≤i ≤n) in the correct order, with arbitrary symbols in between. We prove that Cx is regular by constructing an NFA that accepts ... | diagram | 0.965765 |
OpenReview | ICLR | 2,026 | Grounding Generative Planners in Verifiable Logic: A Hybrid Architecture for Trustworthy Embodied AI | While Large Language Models (LLMs) show immense promise as planners for embodied AI, their stochastic nature and lack of formal reasoning capabilities prevent the strict safety guarantees required for physical deployment. Current approaches fall short: they either rely on other unreliable LLMs for safety checks or simp... | neurosymbolic AI, hybrid AI, formal reasoning, large language models, AI safety, verifiable AI, embodied AI, robotics | neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.) | We propose a hybrid neuro-symbolic architecture where a formal logic verifier tutors an LLM planner, enabling the generation of verifiably safe plans for embodied agents. | [
4,
2,
6,
4
] | Accept (Poster) | Feiyu Wu, Xu Zheng, Yue Qu, Zhuocheng Wang, Zicheng Feng, HUI LI | ~Feiyu_Wu1, ~Xu_Zheng1, ~Yue_Qu4, ~Zhuocheng_Wang1, ~Zicheng_Feng1, ~HUI_LI17 | 20250916 | https://openreview.net/forum?id=wb05ver1k8 | wb05ver1k8 | @inproceedings{
wu2026grounding,
title={Grounding Generative Planners in Verifiable Logic: A Hybrid Architecture for Trustworthy Embodied {AI}},
author={Feiyu Wu and Xu Zheng and Yue Qu and Zhuocheng Wang and Zicheng Feng and HUI LI},
booktitle={The Fourteenth International Conference on Learning Representations},
year... | OpenReview/ICLR/figures/2026/accept_poster/wb05ver1k8/Figure1.png | 1 | Figure 1: The architecture of the Verifiable Iterative Refinement Framework (VIRF). Instead of direct execution, an LLM planner’s actions are verified in a symbolic sandbox against a formal knowledge base. The framework’s core is the Logic Tutor feedback loop, which provides three distinct responses: approval for safe ... | <paragraph_1>Our work introduces the Verifiable Iterative Refinement Framework (VIRF), a novel neurosymbolic architecture designed to govern a generative Large Language Model (LLM) planner. At its core, VIRF transforms the interaction between the stochastic LLM and a deterministic symbolic verifier from a simple pass/f... | diagram | 0.91071 |
OpenReview | ICLR | 2,026 | Characterizing and Optimizing the Spatial Kernel of Multi Resolution Hash Encodings | Multi-Resolution Hash Encoding (MHE), the foundational technique behind Instant Neural Graphics Primitives, provides a powerful parameterization for neural fields. However, its spatial behavior lacks rigorous understanding from a physical systems perspective, leading to reliance on heuristics for hyperparameter selecti... | multi-resolution hash encoding, implicit neural representations, neural fields, point spread function, spatial kernel analysis, anisotropy, resolution limit, FWHM, hash collisions, signal-to-noise ratio, NeRF | applications to computer vision, audio, language, and other modalities | We analyze Multi-Resolution Hash Encoding (MHE) using its Point Spread Function (PSF) to reveal that effective resolution is governed by average, not finest, resolution, and introduce Rotated MHE to mitigate inherent anisotropy and collision noise. | [
4,
6,
6,
4
] | Accept (Poster) | Tianxiang Dai, Jonathan Fan | ~Tianxiang_Dai1, ~Jonathan_Fan1 | 20250920 | https://openreview.net/forum?id=q05hC1Pzkr | q05hC1Pzkr | @inproceedings{
dai2026characterizing,
title={Characterizing and Optimizing the Spatial Kernel of Multi Resolution Hash Encodings},
author={Tianxiang Dai and Jonathan Fan},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=q05hC1Pzkr}
} | OpenReview/ICLR/figures/2026/accept_poster/q05hC1Pzkr/Figure1.png | 1 | Figure 1: Overview of MHE Characterization and Optimization. (a) The MHE architecture utilizes L grid levels with resolutions growing by a factor b. The encoding e(x) is passed to an MLP gθ. We characterize the system by optimizing for a point constraint and measuring the resulting Point Spread Function (PSF). (b) This... | <paragraph_1>In this work, we introduce a novel methodology to characterize and understand the performance of MHE by analyzing its Point Spread Function (PSF). Analogous to measuring the Green’s function of a physical system, the PSF characterizes the model’s response when optimized to represent an idealized point sour... | diagram | 0.984853 |
OpenReview | ICLR | 2,026 | CaTs and DAGs: Integrating Directed Acyclic Graphs with Transformers for Causally Constrained Predictions | Artificial Neural Networks (ANNs), including fully-connected networks and transformers, are highly flexible and powerful function approximators, widely applied in fields like computer vision and natural language processing. However, their inability to inherently respect causal structures can limit their robustness, mak... | transformers, causal inference, causality, inductive bias, DAGs | causal reasoning | Causal Transformers (CaTs) are neural networks constrained by a causal DAG, combining the power of standard ANNs with improved robustness to covariate shift, greater reliability, and interpretability for real-world applications. | [
4,
6,
4
] | Accept (Poster) | Matthew James Vowels, Mathieu Rochat, Sina Akbari | ~Matthew_James_Vowels1, ~Mathieu_Rochat1, ~Sina_Akbari1 | 20250910 | https://openreview.net/forum?id=ZIQactmQxb | ZIQactmQxb | @inproceedings{
vowels2026cats,
title={CaTs and {DAG}s: Integrating Directed Acyclic Graphs with Transformers for Causally Constrained Predictions},
author={Matthew James Vowels and Mathieu Rochat and Sina Akbari},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https:... | OpenReview/ICLR/figures/2026/accept_poster/ZIQactmQxb/Figure8.png | 8 | Figure 8: The DAG used in the real-world psychology example - reconstructed from the causal discovery and domain expertise results presented in (Vowels et al., 2023a). Treatment is attachment style ’attachment’ (also highlighted in orange) and the two outcomes of interest at the measures of depression (highlighted in g... | <paragraph_1>We follow closely the process in (Vowels et al., 2023a) for estimating the causal effect of shifting from one category of attachment style to another on depression. We also report the results for a subset of their analyses in Table 3, which use a ‘naive’ estimator (comprising the bivariate linear model bet... | diagram | 0.913085 |
OpenReview | ICLR | 2,026 | A.I.R.: Enabling Adaptive, Iterative, and Reasoning-based Frame Selection For Video Question Answering | Effectively applying Vision-Language Models (VLMs) to Video Question Answering (VideoQA) hinges on selecting a concise yet comprehensive set of frames, as processing entire videos is computationally infeasible. However, current frame selection methods face a critical trade-off: approaches relying on lightweight similar... | Video Frame Selection, Vision Language Model, Training-Free, Video understanding | applications to computer vision, audio, language, and other modalities | [
6,
4,
6,
4
] | Accept (Poster) | Yuanhao Zou, Shengji Jin, Andong Deng, Youpeng Zhao, Jun Wang, Chen Chen | ~Yuanhao_Zou1, ~Shengji_Jin1, ~Andong_Deng2, ~Youpeng_Zhao2, ~Jun_Wang7, ~Chen_Chen18 | 20250902 | https://openreview.net/forum?id=SZVpOKw0YD | SZVpOKw0YD | @inproceedings{
zou2026air,
title={A.I.R.: Enabling Adaptive, Iterative, and Reasoning-based Frame Selection For Video Question Answering},
author={Yuanhao Zou and Shengji Jin and Andong Deng and Youpeng Zhao and Jun Wang and Chen Chen},
booktitle={The Fourteenth International Conference on Learning Representations},
y... | OpenReview/ICLR/figures/2026/accept_poster/SZVpOKw0YD/Figure2.png | 2 | Figure 2: General pipeline of A.I.R. with three stages: (1) Adaptive Initial Sampling that identifies potential ‘events’ based on query similarity and dynamically samples frames around them using an adaptive budget; (2) Iterative Frame Selection that progressively refines the frame selection via four steps; and (3) QA ... | <paragraph_1>As illustrated in Fig. 2, our proposed approach, A.I.R., performs frame selection in three stages: Adaptive Initial Sampling, Iterative Frame Selection, and QA Stage. The process begins by sampling n frames from the video (containing N total frames) at a fixed frame rate. As a pre-processing step, these n ... | diagram | 0.968053 | |
OpenReview | ICLR | 2,026 | Amortising Inference and Meta-Learning Priors in Neural Networks | One of the core facets of Bayesianism is in the updating of prior beliefs in light of new evidence$\textemdash$so how can we maintain a Bayesian approach if we have no prior beliefs in the first place? This is one of the central challenges in the field of Bayesian deep learning, where it is not clear how to represent b... | neural processes, Bayesian neural networks, meta-learning, priors, variational inference | probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.) | [
4,
6,
4,
6
] | Accept (Poster) | Tommy Rochussen, Vincent Fortuin | ~Tommy_Rochussen1, ~Vincent_Fortuin1 | 20250919 | https://openreview.net/forum?id=KG6SSTz2GJ | KG6SSTz2GJ | @inproceedings{
rochussen2026amortising,
title={Amortising Inference and Meta-Learning Priors in Neural Networks},
author={Tommy Rochussen and Vincent Fortuin},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=KG6SSTz2GJ}
} | OpenReview/ICLR/figures/2026/accept_poster/KG6SSTz2GJ/Figure9.png | 9 | Figure 9: Computational diagrams of the amortised attention layer (a), amortised attention block (b), and BNAM (c). Due to the numerous crossing lines in (a), we colour code the context and target input data paths as orange and light blue respectively. Arbitrarily many amortised attention blocks can be stacked sequenti... | <paragraph_1>We see in Fig. 9(a) that amortised inference can be performed in an attention layer by using amortised linear layers in place of standard linear layers, where MHA is the usual multi-head dot-product attention mechanism acting on keys K, queries Q, and values V. Similarly, in Fig. 9(b) we follow the standar... | diagram | 0.988838 | |
OpenReview | ICLR | 2,026 | DETR-ViP: Detection Transformer with Robust Discriminative Visual Prompts | Visual prompted object detection enables interactive and flexible definition of target categories, thereby facilitating open-vocabulary detection. Since visual prompts are derived directly from image features, they often outperform text prompts in recognizing rare categories. Nevertheless, research on visual prompted d... | object detection, prompt-based detection, open-set object detection | applications to computer vision, audio, language, and other modalities | This paper presents the DETR-ViP framework, which enhances visual prompt detection by improving the semantic consistency of visual prompts and introducing a selective fusion strategy. | [
6,
4,
6
] | Accept (Poster) | Bo Qian, Dahu Shi, Xing Wei | ~Bo_Qian1, ~Dahu_Shi2, ~Xing_Wei5 | 20250903 | https://openreview.net/forum?id=2KKDWERRm3 | 2KKDWERRm3 | @inproceedings{
qian2026detrvip,
title={{DETR}-ViP: Detection Transformer with Robust Discriminative Visual Prompts},
author={Bo Qian and Dahu Shi and Xing Wei},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=2KKDWERRm3}
} | OpenReview/ICLR/figures/2026/accept_poster/2KKDWERRm3/Figure2.png | 2 | Figure 2: The overview of DETR-ViP. DETR-ViP builds on Grounding DINO by incorporating a visual prompt encoder for visual-prompted detection. It improves prompt semantics via global prompt Integration and visual-textual prompt relation distillation, and refines the fusion module to stabilize image-prompt interactions, ... | <paragraph_1>We develop the baseline VIS-GDINO from Grounding DINO by inserting the visual prompt encoder, as defined in Equation (3), between the backbone and the encoder, and removing the fusion modules in the encoder and decoder as represented in Equation (2). On top of this architecture, we introduce the global pro... | diagram | 0.991753 |
OpenReview | ICLR | 2,026 | When Large Multimodal Models Confront Evolving Knowledge: Challenges and Explorations | Large Multimodal Models (LMMs) store vast amounts of pretrained knowledge but struggle to remain aligned with real-world updates, making it difficult to avoid capability degradation when acquiring evolving knowledge. Furthermore, most current work focuses on exploring static textual knowledge injection, neglecting dyna... | Evolving Knowledge Injection; Large multimodal model; Benchmark and Dataset | datasets and benchmarks | This work introduces MMEVOKE benchmark to reveal challenges in knowledge injection and explores potential solutions. | [
6,
6,
4,
8
] | Accept (Poster) | Kailin Jiang, Yuntao Du, Yukai Ding, Yuchen Ren, Ning Jiang, Zhi Gao, Zilong Zheng, Lei Liu, Bin Li, Qing Li | ~Kailin_Jiang1, ~Yuntao_Du2, ~Yukai_Ding2, ~Yuchen_Ren1, ~Ning_Jiang7, ~Zhi_Gao5, ~Zilong_Zheng1, ~Lei_Liu28, ~Bin_Li8, ~Qing_Li1 | 20250901 | https://openreview.net/forum?id=iaPEM00wEs | iaPEM00wEs | @inproceedings{
jiang2026when,
title={When Large Multimodal Models Confront Evolving Knowledge: Challenges and Explorations},
author={Kailin Jiang and Yuntao Du and Yukai Ding and Yuchen Ren and Ning Jiang and Zhi Gao and Zilong Zheng and Lei Liu and Bin Li and Qing Li},
booktitle={The Fourteenth International Conferen... | OpenReview/ICLR/figures/2026/accept_poster/iaPEM00wEs/Figure25.png | 25 | Figure 25: Fine-grained dimensional results on MathVision and HallusionBench. | <paragraph_1>According to Figures 22, 23, 24, 25, and 26, we conduct result analysis for each benchmark.</paragraph_1> | diagram | 0.915522 |
OpenReview | ICLR | 2,026 | Robustness in the Face of Partial Identifiability in Reward Learning | In Reward Learning (ReL), we are given feedback on an unknown target reward, and the goal is to use this information to recover it in order to carry out some downstream application, e.g., planning. When the feedback is not informative enough, the target reward is only partially identifiable, i.e., there exists a set of... | Inverse Reinforcement Learning, Reward Learning, Preference Based Reinforcement Learning, Theory | reinforcement learning | We propose to tackle the identifiability problem in reward learning with a robust approach. | [
4,
2,
8,
8,
8
] | Accept (Poster) | Filippo Lazzati, Alberto Maria Metelli | ~Filippo_Lazzati2, ~Alberto_Maria_Metelli2 | 20250918 | https://openreview.net/forum?id=e4xANXjA9W | e4xANXjA9W | @inproceedings{
lazzati2026robustness,
title={Robustness in the Face of Partial Identifiability in Reward Learning},
author={Filippo Lazzati and Alberto Maria Metelli},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=e4xANXjA9W}
} | OpenReview/ICLR/figures/2026/accept_poster/e4xANXjA9W/Figure2.png | 2 | Figure 2: Illustration of the quantities of interest. r is any reward. | <paragraph_1>See Figure 2 for a simple graphical intuition of all these quantities.</paragraph_1> | diagram | 0.966508 |
OpenReview | ICLR | 2,026 | Unveiling the Mechanism of Continuous Representation Full-Waveform Inversion: A Wave Based Neural Tangent Kernel Framework | Full-waveform inversion (FWI) estimates physical parameters in the wave equation from limited measurements and has been widely applied in geophysical exploration, medical imaging, and non-destructive testing. Conventional FWI methods are limited by their notorious sensitivity to the accuracy of the initial models. Rece... | Full-waveform inversion; Continuous representation; Implicit neural representation; Neural tangent kernel | applications to physical sciences (physics, chemistry, biology, etc.) | This paper develops a theoretical framework to explain and optimize continuous representation FWI methods, and based on this, proposes some novel hybrid representations that strike a better balance between robustness and high-frequency convergence. | [
6,
8,
8,
4
] | Accept (Poster) | Ruihua Chen, Yisi Luo, Bangyu Wu, Deyu Meng | ~Ruihua_Chen1, ~Yisi_Luo1, ~Bangyu_Wu1, ~Deyu_Meng1 | 20250915 | https://openreview.net/forum?id=blqYa21WOv | blqYa21WOv | @inproceedings{
chen2026unveiling,
title={Unveiling the Mechanism of Continuous Representation Full-Waveform Inversion: A Wave Based Neural Tangent Kernel Framework},
author={Ruihua Chen and Yisi Luo and Bangyu Wu and Deyu Meng},
booktitle={The Fourteenth International Conference on Learning Representations},
year={202... | OpenReview/ICLR/figures/2026/accept_poster/blqYa21WOv/Figure3.png | 3 | Figure 3: Pipeline of CR-FWI. CR-FWI employs (a) implicit neural representation, (b) low rank tensor function, or (c) multi-grid parametric encoding to represent the velocity parameter model and integrate the wave equation in a loop. | <paragraph_1>• Theory: We develop a unified wave-based NTK framework for conventional FWI and CR-FWI. The eigenvalue analysis explains why CR-FWI reduces reliance on initial models and exhibits slower high-frequency convergence, with numerical tests confirming these insights (see Fig. 5). • Method: Inspired by the eigenv... | diagram | 0.964798 |
OpenReview | ICLR | 2,026 | BindWeave: Subject-Consistent Video Generation via Cross-Modal Integration | Diffusion Transformer has shown remarkable abilities in generating high-fidelity videos, delivering visually coherent frames and rich details over extended durations.
However, existing video generation models still fall short in subject-consistent video generation due to an inherent difficulty in parsing prompts that s... | Video generation, Diffusion models | generative models | [
4,
6,
4,
6
] | Accept (Poster) | Zhaoyang Li, Dongjun Qian, Kai Su, qishuai diao, Xiangyang Xia, Chang Liu, Wenfei Yang, Tianzhu Zhang, Zehuan Yuan | ~Zhaoyang_Li7, ~Dongjun_Qian1, ~Kai_Su1, ~qishuai_diao1, ~Xiangyang_Xia1, ~Chang_Liu71, ~Wenfei_Yang2, ~Tianzhu_Zhang1, ~Zehuan_Yuan1 | 20250919 | https://openreview.net/forum?id=FP2XNyV9WL | FP2XNyV9WL | @inproceedings{
li2026bindweave,
title={BindWeave: Subject-Consistent Video Generation via Cross-Modal Integration},
author={Zhaoyang Li and Dongjun Qian and Kai Su and qishuai diao and Xiangyang Xia and Chang Liu and Wenfei Yang and Tianzhu Zhang and Zehuan Yuan},
booktitle={The Fourteenth International Conference on ... | OpenReview/ICLR/figures/2026/accept_poster/FP2XNyV9WL/Figure2.png | 2 | Figure 2: Framework of our method. A multimodal large language model performs cross-modal reasoning to ground entities and disentangle roles, attributes, and interactions from the prompt and optional reference images. The resulting subject-aware signals condition a Diffusion Transformer through cross-attention and ligh... | <paragraph_1>Our proposed BindWeave is designed to overcome the limitations of shallow fusion paradigms in subject-consistent video generation. The core principle of our approach is to replace shallow, posthoc fusion with a deep, reasoned understanding of multimodal inputs before the generation process begins. To this ... | diagram | 0.939486 | |
OpenReview | ICLR | 2,026 | FideDiff: Efficient Diffusion Model for High-Fidelity Image Motion Deblurring | Recent advancements in image motion deblurring, driven by CNNs and transformers, have made significant progress. Large-scale pre-trained diffusion models, which are rich in real-world modeling, have shown great promise for high-quality image restoration tasks such as deblurring, demonstrating stronger generative capabi... | Image Motion-Deblurring, Diffusion Model | applications to computer vision, audio, language, and other modalities | [
6,
8,
6,
4
] | Accept (Poster) | Xiaoyang Liu, Zhengyan Zhou, Zihang Xu, Jiezhang Cao, Zheng Chen, Yulun Zhang | ~Xiaoyang_Liu4, ~Zhengyan_Zhou2, ~Zihang_Xu11, ~Jiezhang_Cao2, ~Zheng_Chen11, ~Yulun_Zhang1 | 20250906 | https://openreview.net/forum?id=AFJMB9SkHT | AFJMB9SkHT | @inproceedings{
liu2026fidediff,
title={FideDiff: Efficient Diffusion Model for High-Fidelity Image Motion Deblurring},
author={Xiaoyang Liu and Zhengyan Zhou and Zihang Xu and Jiezhang Cao and Zheng Chen and Yulun Zhang},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url... | OpenReview/ICLR/figures/2026/accept_poster/AFJMB9SkHT/Figure3.png | 3 | Figure 3: Forward and backward processes. | <paragraph_1>In Fig. 3, we reformulate the forward and backward processes for the image motion blurring and deblurring. We define the clean image as z0 and the initial blur kernel as identity convolution k0, where z0 = z0 ∗k0. From a pure clean image to the blurry image, we regard the forward blur kernel generation pro... | diagram | 0.985859 | |
OpenReview | ICLR | 2,026 | SPELL: Self-Play Reinforcement Learning for evolving Long-Context Language Models | Progress in long-context reasoning for large language models (LLMs) has lagged behind other recent advances. This gap arises not only from the intrinsic difficulty of processing long texts, but also from the scarcity of reliable human annotations and programmatically verifiable reward signals. In this paper, we propose... | Self-Play, Reinforcement Learning, Long-Context Reasoning, Large Language Models | reinforcement learning | A label-free RL framework that drives the autonomous evolution of LLMs in long-context reasoning | [
6,
6,
4,
8
] | Accept (Poster) | Ziyi Yang, Weizhou Shen, Chenliang Li, Ruijun Chen, Fanqi Wan, Ming Yan, Xiaojun Quan, Fei Huang | ~Ziyi_Yang6, ~Weizhou_Shen1, ~Chenliang_Li2, ~Ruijun_Chen4, ~Fanqi_Wan1, ~Ming_Yan2, ~Xiaojun_Quan1, ~Fei_Huang2 | 20250916 | https://openreview.net/forum?id=83F6YF4Hz6 | 83F6YF4Hz6 | @inproceedings{
yang2026spell,
title={{SPELL}: Self-Play Reinforcement Learning for evolving Long-Context Language Models},
author={Ziyi Yang and Weizhou Shen and Chenliang Li and Ruijun Chen and Fanqi Wan and Ming Yan and Xiaojun Quan and Fei Huang},
booktitle={The Fourteenth International Conference on Learning Repre... | OpenReview/ICLR/figures/2026/accept_poster/83F6YF4Hz6/Figure1.png | 1 | Figure 1: (Left) An overview of the SPELL framework, where a single LLM self-evolves by dynamically adopting the roles of questioner, responder, and verifier. (Right) SPELL consistently boosts performance across various models (top) and exhibits superior test-time scaling over traditional RLVR (bottom). | <paragraph_1>As illustrated in Figure 2 and Algorithm 1, SPELL proceeds iteratively: given a cluster of n documents C = {ci}n i=1 and a task type1 τ, the policy πθ first generates new questions,2 then attempts to solve them, and finally verifies the solutions before performing a unified policy update.</paragraph_1> | diagram | 0.988391 |
OpenReview | ICLR | 2,026 | Accelerating Benchmarking of Functional Connectivity Modeling via Structure-aware Core-set Selection | Benchmarking the hundreds of functional connectivity (FC) modeling methods on large-scale fMRI datasets is critical for reproducible neuroscience. However, the combinatorial explosion of model–data pairings makes exhaustive evaluation computationally prohibitive, preventing such assessments from becoming a routine pre-... | Functional Connectivity Benchmark, Core-set Selection, Network Modeling, Structure-aware Sampling | applications to neuroscience & cognitive science | We frame functional connectivity benchmarking task as a ranking recommendation problem and propose a self-supervised core-set selection framework that achieves up to 23.2% higher ranking stability than baselines at a 10% sampling rate. | [
6,
6,
6,
6
] | Accept (Poster) | Ling Zhan, Zhen Li, Junjie Huang, Tao Jia | ~Ling_Zhan2, ~Zhen_Li38, ~Junjie_Huang4, ~Tao_Jia3 | 20250907 | https://openreview.net/forum?id=0RYazbfSzW | 0RYazbfSzW | @inproceedings{
zhan2026accelerating,
title={Accelerating Benchmarking of Functional Connectivity Modeling via Structure-aware Core-set Selection},
author={Ling Zhan and Zhen Li and Junjie Huang and Tao Jia},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://open... | OpenReview/ICLR/figures/2026/accept_poster/0RYazbfSzW/Figure1.png | 1 | Figure 1: Overview of the SCLCS framework for ranking-preserving core-set selection. Contrasting with selection for single-model classification (top left), our task is to preserve the performance ranking of SPIs (top right). Our method (bottom) achieves this using a Transformer to learn structures, our novel SPS metric... | <paragraph_1>While core-set selection is well studied, most existing methods target a different goal: constructing a small training proxy for a single predictive model (Feldman, 2020; Lee et al., 2024; Hong et al., 2024b). In our setting (Figure 1), the core-set must preserve the relative performance ranking across hun... | diagram | 0.941608 |
OpenReview | ICLR | 2,026 | Tackling Time-Series Forecasting Generalization via Mitigating Concept Drift | Time-series forecasting finds broad applications in real-world scenarios. Due to the dynamic nature of time series data, it is important for time-series forecasting models to handle potential distribution shifts over time. In this paper, we initially identify two types of distribution shifts in time series: concept dri... | Time-Series Forecasting, Distribution Shift, Concept Drift | learning on time series and dynamical systems | [
6,
6,
6
] | Accept (Poster) | Zhiyuan Zhao, Haoxin Liu, B. Aditya Prakash | ~Zhiyuan_Zhao1, ~Haoxin_Liu3, ~B._Aditya_Prakash2 | 20250914 | https://openreview.net/forum?id=emkvZ7NanK | emkvZ7NanK | @inproceedings{
zhao2026tackling,
title={Tackling Time-Series Forecasting Generalization via Mitigating Concept Drift},
author={Zhiyuan Zhao and Haoxin Liu and B. Aditya Prakash},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=emkvZ7Nan... | OpenReview/ICLR/figures/2026/accept_poster/emkvZ7NanK/Figure1.png | 1 | Figure 1: Comparison between conventional time-series forecasting and our approach. Our approach identifies invariant patterns in lookback and horizon window as XSUR and then models a stable conditional distribution accordingly to mitigate concept drift. | <paragraph_1>This instability arises because, for a given exogenous feature X, its lookback window XL alone may lack sufficient information to predict YH, while learning a stable conditional distribution requires that the inputs provide sufficient information to predict the output (Sagawa et al., 2019; Arjovsky et al.,... | diagram | 0.94529 | |
OpenReview | ICLR | 2,026 | SRT: Super-Resolution for Time Series via Disentangled Rectified Flow | Fine-grained time series data with high temporal resolution is critical for accurate analytics across a wide range of applications. However, the acquisition of such data is often limited by cost and feasibility. This problem can be tackled by reconstructing high-resolution signals from low-resolution inputs based on sp... | Time Series Super-Resolution, Rectified Flow, Temporal Disentanglement, Implicit Neural Representations | learning on time series and dynamical systems | We propose SRT, a novel disentangled rectified flow framework for time series super-resolution that generates high-resolution details from low-resolution data, achieving state-of-the-art performance across nine benchmarks. | [
4,
6,
4,
8
] | Accept (Poster) | Jufang Duan, Shenglong Xiao, Yuren Zhang | ~Jufang_Duan2, ~Shenglong_Xiao1, ~Yuren_Zhang4 | 20250920 | https://openreview.net/forum?id=I94Eg6cu7P | I94Eg6cu7P | @inproceedings{
duan2026srt,
title={{SRT}: Super-Resolution for Time Series via Disentangled Rectified Flow},
author={Jufang Duan and Shenglong Xiao and Yuren Zhang},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=I94Eg6cu7P}
} | OpenReview/ICLR/figures/2026/accept_poster/I94Eg6cu7P/Figure2.png | 2 | Figure 2: Architecture of our proposed SRT. The upper left shows the training process, where the true residual sequence is decomposed, and the velocity predictors (Vs and Vτ ) are trained to fit the difference between the true values of s and τ and their respective initial states. The lower left depicts the inference p... | <paragraph_1>We summarize the aforementioned workflow in Figure 2.</paragraph_1> | diagram | 0.87616 |
OpenReview | ICLR | 2,026 | Distilling and Adapting: A Topology-Aware Framework for Zero-Shot Interaction Prediction in Multiplex Biological Networks | Multiplex Biological Networks (MBNs), which represent multiple interaction types between entities, are crucial for understanding complex biological systems. Yet, existing methods often inadequately model multiplexity, struggle to integrate structural and sequence information, and face difficulties in zero-shot predicti... | Graph representation learning, contrastive learning, multiplex networks, knowledge distillation, zero-shot prediction | applications to physical sciences (physics, chemistry, biology, etc.) | [
6,
4,
6
] | Accept (Poster) | Alana Deng, Sugitha Janarthanan, Yan Sun, Zihao Jing, Pingzhao Hu | ~Alana_Deng1, ~Sugitha_Janarthanan1, ~Yan_Sun11, ~Zihao_Jing1, ~Pingzhao_Hu2 | 20250918 | https://openreview.net/forum?id=GvK1y3xqmh | GvK1y3xqmh | @inproceedings{
deng2026distilling,
title={Distilling and Adapting: A Topology-Aware Framework for Zero-Shot Interaction Prediction in Multiplex Biological Networks},
author={Alana Deng and Sugitha Janarthanan and Yan Sun and Zihao Jing and Pingzhao Hu},
booktitle={The Fourteenth International Conference on Learning R... | OpenReview/ICLR/figures/2026/accept_poster/GvK1y3xqmh/Figure3.png | 3 | Figure 3: Illustration of the CAE module. | <paragraph_1>The CAE module refines multiplex embeddings through node- and layer-level inter-layer attention combined with contrastive learning (Figure 3 and Supplementary Section C.). Each layer is encoded with a Graph Transformer, and inter-layer attention enables nodes to adaptively attend to counterparts across int... | diagram | 0.974683 | |
OpenReview | ICLR | 2,026 | CroCoDiLight: Repurposing Cross-View Completion Encoders for Relighting | Cross-view completion (CroCo) has proven effective as pre-training for geometric downstream tasks such as stereo depth, optical flow, and point cloud prediction. In this paper we show that it also learns photometric understanding due to training pairs with differing illumination. We propose a method to disentangle CroC... | cross-view completion, relighting, intrinsic image estimation, albedo estimation, shadow removal | unsupervised, self-supervised, semi-supervised, and supervised representation learning | Disentangle CroCo latents into lighting and scene intrinsics, edit lighting for shadow removal, albedo estimation, relighting and lighting interpolation. | [
4,
8,
4,
4
] | Accept (Poster) | Alistair J Foggin, William A P Smith | ~Alistair_J_Foggin1, ~William_A_P_Smith1 | 20250903 | https://openreview.net/forum?id=GKvb3HCyNk | GKvb3HCyNk | @inproceedings{
foggin2026crocodilight,
title={CroCoDiLight: Repurposing Cross-View Completion Encoders for Relighting},
author={Alistair J Foggin and William A P Smith},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=GKvb3HCyNk}
} | OpenReview/ICLR/figures/2026/accept_poster/GKvb3HCyNk/Figure2.png | 2 | Figure 2: The architecture of the model comprises four main components. First is the frozen CroCo encoder. Last is the decoder D which is separately pre-trained and then frozen to decode from CroCo latent space to RGB. Then there are the delighting and relighting transformers, I and R respectively, which disentangle li... | <paragraph_1>Our approach (see Fig. 2) starts with a delighting transformer which disentangles illumination from scene intrinsic properties by translating the patch latents into intrinsic patches and estimating a lighting latent vector that describes the appearance in that particular illumination environment. Second, a... | diagram | 0.998946 |
OpenReview | ICLR | 2,026 | Beyond Prompt-Induced Lies: Investigating LLM Deception on Benign Prompts | Large Language Models (LLMs) are widely deployed in reasoning, planning, and decision-making tasks, making their trustworthiness critical. A significant and underexplored risk is intentional deception, where an LLM deliberately fabricates or conceals information to serve a hidden objective. Existing studies typically i... | Large Language Model, Deception, Lie, Honest, Trustworthy | alignment, fairness, safety, privacy, and societal considerations | We detected the widespread deception of LLM under benign prompts and found its tendency increases with task difficulty. | [
6,
6,
8
] | Accept (Oral) | Zhaomin Wu, Mingzhe Du, See-Kiong Ng, Bingsheng He | ~Zhaomin_Wu1, ~Mingzhe_Du1, ~See-Kiong_Ng1, ~Bingsheng_He1 | 20250917 | https://openreview.net/forum?id=PDBBYwd1LY | PDBBYwd1LY | @inproceedings{
wu2026beyond,
title={Beyond Prompt-Induced Lies: Investigating {LLM} Deception on Benign Prompts},
author={Zhaomin Wu and Mingzhe Du and See-Kiong Ng and Bingsheng He},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=PDBB... | OpenReview/ICLR/figures/2026/accept_oral/PDBBYwd1LY/Figure2.png | 2 | Figure 2: An illustration of Contact Searching Questions (CSQ), featuring a linked-list question (left) and a broken-list question (right). Given the full-length question, Answer 1 represents the model’s expression. For the shorter follow-up question, Answer 2 reflects its underlying belief. | <paragraph_1>LLM deception can arise in two settings: (1) an incentivizing prompt is given, and the model lies to satisfy the objective specified in the prompt (see Figure 1); (2) a benign prompt is given, yet the model lies due to its intrinsic objective. Most existing studies focus on the incentivizing prompt: for ex... | diagram | 0.986552 |
OpenReview | ICLR | 2,026 | The Shape of Adversarial Influence: Characterizing LLM Latent Spaces with Persistent Homology | Existing interpretability methods for Large Language Models (LLMs) often fall short by focusing on linear directions or isolated features, overlooking the high-dimensional, nonlinear, and relational geometry within model representations. This study focuses on how adversarial inputs systematically affect the internal re... | Persistent Homology, Interpretability, Topological Data Analysis, Representation Geometry, Large Language Models, AI Security, Adversarial Attacks, Sparse Autoencoders | interpretability and explainable AI | We use persistent homology to interpret how adversarial inputs reshape LLM representation spaces, resulting in a robust signature that provides multiscale, geometry-aware insights complementary to standard interpretability methods. | [
8,
6,
6,
4
] | Accept (Oral) | Aideen Fay, Inés García-Redondo, Qiquan Wang, Haim Dubossarsky, Anthea Monod | ~Aideen_Fay1, ~Inés_García-Redondo1, ~Qiquan_Wang2, ~Haim_Dubossarsky1, ~Anthea_Monod1 | 20250919 | https://openreview.net/forum?id=v2PglvLLKT | v2PglvLLKT | @inproceedings{
fay2026the,
title={The Shape of Adversarial Influence: Characterizing {LLM} Latent Spaces with Persistent Homology},
author={Aideen Fay and In{\'e}s Garc{\'\i}a-Redondo and Qiquan Wang and Haim Dubossarsky and Anthea Monod},
booktitle={The Fourteenth International Conference on Learning Representations}... | OpenReview/ICLR/figures/2026/accept_oral/v2PglvLLKT/Figure4.png | 4 | Figure 4: Pipeline for local analysis. | <paragraph_1>computational cost of PH and to enable statistically robust inference. Subsampling approaches in PH are theoretically grounded, as under mild sampling models, persistence diagrams estimated from point clouds converge to the population diagrams with guaranteed rates (Chazal et al., 2015; 2014). For each mod... | diagram | 0.955924 |
OpenReview | ICLR | 2,026 | Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learning | Causal discovery with latent variables is a fundamental task. Yet most existing methods rely on strong structural assumptions, such as enforcing specific indicator patterns for latents or restricting how they can interact with others. We argue that a core obstacle to a general, structural-assumption-free approach is th... | causal discovery, latent variables, equivalence, rank constraints, linear non-Gaussian models, cycles | causal reasoning | [
8,
8,
8,
8
] | Accept (Oral) | Haoyue Dai, Immanuel Albrecht, Peter Spirtes, Kun Zhang | ~Haoyue_Dai1, ~Immanuel_Albrecht1, ~Peter_Spirtes1, ~Kun_Zhang1 | 20250915 | https://openreview.net/forum?id=b8TlYh6PN6 | b8TlYh6PN6 | @inproceedings{
dai2026distributional,
title={Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learning},
author={Haoyue Dai and Immanuel Albrecht and Peter Spirtes and Kun Zhang},
booktitle={The Fourteenth International Conference on Learning Representations}... | OpenReview/ICLR/figures/2026/accept_oral/b8TlYh6PN6/Figure8.png | 8 | Figure 8: Presentation of the equivalence class that glvLiNG estimates from the stock market data. Different colors of nodes indicate different sectors. Solid and dashed edges indicate edges that must appear in all or at least one equivalent graph(s). | <paragraph_1>By applying glvLiNG on this dataset, we recovered an equivalence class of causal graphs containing 2 latent variables. The presentation (see Appendix C.3) of this equivalence class is shown in Figure 8. Here is a summary: the class consists of 19,008 causal graphs with 16=14+2 vertices, and among them the ... | diagram | 0.985522 | |
OpenReview | ICLR | 2,026 | Triple-BERT: Do We Really Need MARL for Order Dispatch on Ride-Sharing Platforms? | On-demand ride-sharing platforms, such as Uber and Lyft, face the intricate real-time challenge of bundling and matching passengers—each with distinct origins and destinations—to available vehicles, all while navigating significant system uncertainties. Due to the extensive observation space arising from the large numb... | Reinforcement Learning, Order Dispatching, Ride Sharing | reinforcement learning | This paper proposes a novel centralized reinforcement learning framework for large-scale order dispatching tasks in ride-sharing scenarios, achieving better cooperation among workers compared to previous multi-agent methods. | [
8,
6,
6,
6
] | Accept (Oral) | Zijian Zhao, Sen Li | ~Zijian_Zhao7, ~Sen_Li5 | 20250918 | https://openreview.net/forum?id=symgW6FhA6 | symgW6FhA6 | @inproceedings{
zhao2026triplebert,
title={Triple-{BERT}: Do We Really Need {MARL} for Order Dispatch on Ride-Sharing Platforms?},
author={Zijian Zhao and Sen Li},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=symgW6FhA6}
} | OpenReview/ICLR/figures/2026/accept_oral/symgW6FhA6/Figure5.png | 5 | Figure 5: Network Structure in Stage 1 | <paragraph_1>In stage 1, the network structure is shown as Fig. 5, which is consisted by the encoders and the QK-Attention module of proposed network in Fig. 2. Although the model takes the entire worker and</paragraph_1> | diagram | 0.997812 |
OpenReview | ICLR | 2,017 | HyperNetworks | This work explores hypernetworks: an approach of using one network, also known as a hypernetwork, to generate the weights for another network. We apply hypernetworks to generate adaptive weights for recurrent networks. In this case, hypernetworks can be viewed as a relaxed form of weight-sharing across layers. In our ... | Natural language processing, Deep learning, Supervised Learning | We train a small RNN to generate weights for a larger RNN, and train the system end-to-end. We obtain state-of-the-art results on a variety of sequence modelling tasks. | [
6,
7,
8,
9
] | Accept (Poster) | David Ha, Andrew M. Dai, Quoc V. Le | hadavid@google.com, adai@google.com, qvl@google.com | 20161027 | https://openreview.net/forum?id=rkpACe1lx | rkpACe1lx | @inproceedings{
ha2017hypernetworks,
title={HyperNetworks},
author={David Ha and Andrew M. Dai and Quoc V. Le},
booktitle={International Conference on Learning Representations},
year={2017},
url={https://openreview.net/forum?id=rkpACe1lx}
} | OpenReview/ICLR/figures/2017/accept_poster/rkpACe1lx/Figure1.png | 1 | Figure 1: An overview of HyperRNNs. Black connections and parameters are associated basic RNNs. Orange connections and parameters are introduced in this work and associated with HyperRNNs. Dotted arrows are for parameter generation. | <paragraph_1>In HyperRNN, we allow Wh and Wx to float over time by using a smaller hypernetwork to generate these parameters of the main RNN at each step (see Figure 1). More concretely, the parameters Wh, Wx, b of the main RNN are different at different time steps, so that ht can now be computed as:</paragraph_1> | diagram | 0.998998 | |
OpenReview | ICLR | 2,017 | Predicting Medications from Diagnostic Codes with Recurrent Neural Networks | It is a surprising fact that electronic medical records are failing at one of their primary purposes, that of tracking the set of medications that the patient is actively taking. Studies estimate that up to 50% of such lists omit active drugs, and that up to 25% of all active medications do not appear on the appropriat... | Deep learning, Supervised Learning, Applications | Applying recurrent neural networks to fix errors and omissions in patient medication records. | [
8,
6,
7
] | Accept (Poster) | Jacek M. Bajor, Thomas A. Lasko | jacek.m.bajor@vanderbilt.edu, tom.lasko@vanderbilt.edu | 20161103 | https://openreview.net/forum?id=rJEgeXFex | rJEgeXFex | @inproceedings{
bajor2017predicting,
title={Predicting Medications from Diagnostic Codes with Recurrent Neural Networks},
author={Jacek M. Bajor and Thomas A. Lasko},
booktitle={International Conference on Learning Representations},
year={2017},
url={https://openreview.net/forum?id=rJEgeXFex}
} | OpenReview/ICLR/figures/2017/accept_poster/rJEgeXFex/Figure1.png | 1 | Figure 1: Simplified representation of a recurrent neural network (left) and an unrolled recurrent neural network (right). xi is a single element in an input sequence x, hi is an output after a single pass through the recurrent unit. Adapted from Olah (2015). | <paragraph_1>A recurrent neural network is a variation in which the output of one node on input xt loops around to become an input to another node on input xt+1, allowing information to be preserved as it iterates over an input data sequence (Figure 1). They were introduced in the 1980s (Rumelhart et al., 1986), but ac... | diagram | 0.982775 | |
OpenReview | ICLR | 2,017 | Episodic Exploration for Deep Deterministic Policies for StarCraft Micromanagement | We consider scenarios from the real-time strategy game StarCraft as benchmarks for reinforcement learning algorithms. We focus on micromanagement, that is, the short-term, low-level control of team members during a battle. We propose several scenarios that are challenging for reinforcement learning algorithms because t... | Deep learning, Reinforcement Learning, Games | We propose a new reinforcement learning algorithm based on zero order optimization, that we evaluate on StarCraft micromanagement scenarios. | [
8,
7,
7
] | Accept (Poster) | Nicolas Usunier, Gabriel Synnaeve, Zeming Lin, Soumith Chintala | usunier@fb.com, gab@fb.com, zlin@fb.com, soumith@fb.com | 20161104 | https://openreview.net/forum?id=r1LXit5ee | r1LXit5ee | @inproceedings{
usunier2017episodic,
title={Episodic Exploration for Deep Deterministic Policies for StarCraft Micromanagement},
author={Nicolas Usunier and Gabriel Synnaeve and Zeming Lin and Soumith Chintala},
booktitle={International Conference on Learning Representations},
year={2017},
url={https://openreview.net/f... | OpenReview/ICLR/figures/2017/accept_poster/r1LXit5ee/Figure1.png | 1 | Figure 1: Representation of the joint (state, command) featurization and scoring process. | <paragraph_1>The full scoring approach is depicted in Figure 1. In our approach, a state is represented as a list of units. The raw features are transformed by a featurizer that 1) takes the 3 unit features (pos, tgt_pos and next_pos) and computes their distances with the position the acting unit and its target (posc a... | diagram | 0.9344 | |
OpenReview | ICLR | 2,017 | Calibrating Energy-based Generative Adversarial Networks | In this paper, we propose to equip Generative Adversarial Networks with the ability to produce direct energy estimates for samples.
Specifically, we propose a flexible adversarial training framework, and prove this framework not only ensures the generator converges to the true data distribution, but also enables the di... | Deep learning | [
8,
8,
7
] | Accept (Poster) | Zihang Dai, Amjad Almahairi, Philip Bachman, Eduard Hovy, Aaron Courville | zander.dai@gmail.com, amjadmahayri@gmail.com, phil.bachman@gmail.com, hovy@cmu.edu, aaron.courville@gmail.com | 20161104 | https://openreview.net/forum?id=SyxeqhP9ll | SyxeqhP9ll | @inproceedings{
dai2017calibrating,
title={Calibrating Energy-based Generative Adversarial Networks},
author={Zihang Dai and Amjad Almahairi and Philip Bachman and Eduard Hovy and Aaron Courville},
booktitle={International Conference on Learning Representations},
year={2017},
url={https://openreview.net/forum?id=Syxeqh... | OpenReview/ICLR/figures/2017/accept_poster/SyxeqhP9ll/Figure4.png | 4 | Figure 4: 100 highest-ranked images out of 1000 generated and reals (bounding box) samples. | <paragraph_1>digit from the NIST dataset. 2 We compare the ability of EGAN-Ent-NN with both EGAN-Const and GAN on ranking a set of 1,000 images, half of which are generated samples and the rest are real test images. Figures 4 and 5 show the top-100 and bottom-100 ranked images respectively for each model, after trainin... | diagram | 0.880793 | ||
OpenReview | ICLR | 2,017 | On Detecting Adversarial Perturbations | Machine learning and deep learning in particular has advanced tremendously on perceptual tasks in recent years. However, it remains vulnerable against adversarial perturbations of the input that have been crafted specifically to fool the system while being quasi-imperceptible to a human. In this work, we propose to au... | Computer vision, Deep learning, Supervised Learning | We present and evaluate an approach for detecting adversarial perturbations in images based on attaching a small subnetwork to a deep neural network that is trained specifically to detect adversarial perturbations. | [
5,
7,
7
] | Accept (Poster) | Jan Hendrik Metzen, Tim Genewein, Volker Fischer, Bastian Bischoff | JanHendrik.Metzen@de.bosch.com, Tim.Genewein@de.bosch.com, Volker.Fischer@de.bosch.com, Bastian.Bischoff@de.bosch.com | 20161104 | https://openreview.net/forum?id=SJzCSf9xg | SJzCSf9xg | @inproceedings{
metzen2017on,
title={On Detecting Adversarial Perturbations},
author={Jan Hendrik Metzen and Tim Genewein and Volker Fischer and Bastian Bischoff},
booktitle={International Conference on Learning Representations},
year={2017},
url={https://openreview.net/forum?id=SJzCSf9xg}
} | OpenReview/ICLR/figures/2017/accept_poster/SJzCSf9xg/Figure1.png | 1 | Figure 1: (Top) ResNet used for classification. Numbers on top of arrows denote the number of feature maps and numbers below arrows denote spatial resolutions. Conv denotes a convolutional layer, Res∗5 denotes a sequence of 5 residual blocks as introduced by He et al. (2016), GAP denotes a global-average pooling layer ... | <paragraph_1>We use a 32-layer Residual Network (He et al., 2016, ResNet) as classifier. The structure of the network is shown in Figure 1. The network has been trained for 100 epochs with stochastic gradient descent and momentum on 45000 data points from the train set. The momentum term was set to 0.9 and the initial l... | diagram | 0.994278 | |
OpenReview | ICLR | 2,017 | Learning to Remember Rare Events | Despite recent advances, memory-augmented deep neural networks are still limited
when it comes to life-long and one-shot learning, especially in remembering rare events.
We present a large-scale life-long memory module for use in deep learning.
The module exploits fast nearest-neighbor algorithms for efficiency and
thu... | Deep learning | We introduce a memory module for life-long learning that adds one-shot learning capability to any supervised neural network. | [
7,
8,
6
] | Accept (Poster) | Lukasz Kaiser, Ofir Nachum, Aurko Roy, Samy Bengio | lukaszkaiser@google.com, ofirnachum@google.com, aurko@gatech.edu, bengio@google.com | 20161104 | https://openreview.net/forum?id=SJTQLdqlg | SJTQLdqlg | @inproceedings{
kaiser2017learning,
title={Learning to Remember Rare Events},
author={Lukasz Kaiser and Ofir Nachum and Aurko Roy and Samy Bengio},
booktitle={International Conference on Learning Representations},
year={2017},
url={https://openreview.net/forum?id=SJTQLdqlg}
} | OpenReview/ICLR/figures/2017/accept_poster/SJTQLdqlg/Figure3.png | 3 | Figure 3: Extended Neural GPU with memory module. Memory query is read from the position one below the current output logit, and the embedded memory value is put at the same position of the output tape p. The network learns to use these values to produce the output in the next step. | <paragraph_1>Extended Neural GPU with Memory. To test versatility of our memory module, we also add it to the Extended Neural GPU, a convolutional-recurrent model introduced by Kaiser & Bengio (2016). The Extended Neural GPU is a sequence-to-sequence model too, but its decoder is convolutional and the size of its state... | diagram | 0.974898 | |
OpenReview | ICLR | 2,017 | Deep Probabilistic Programming | We propose Edward, a Turing-complete probabilistic programming language. Edward defines two compositional representations—random variables and inference. By treating inference as a first class citizen, on a par with modeling, we show that probabilistic programming can be as flexible and computationally efficient as tra... | [
5,
8,
7
] | Accept (Poster) | Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei | dustin@cs.columbia.edu, mathoffm@adobe.com, rif@google.com, ebrevdo@google.com, kpmurphy@google.com, david.blei@columbia.edu | 20161104 | https://openreview.net/forum?id=Hy6b4Pqee | Hy6b4Pqee | @inproceedings{
tran2017deep,
title={Deep Probabilistic Programming},
author={Dustin Tran and Matthew D. Hoffman and Rif A. Saurous and Eugene Brevdo and Kevin Murphy and David M. Blei},
booktitle={International Conference on Learning Representations},
year={2017},
url={https://openreview.net/forum?id=Hy6b4Pqee}
} | OpenReview/ICLR/figures/2017/accept_poster/Hy6b4Pqee/Figure10.png | 10 | Figure 10: Bayesian neural network for classification. | <paragraph_1>where NN is a 2-layer neural network whose weights and biases form the latent variables W0, b0, W1, b1. Define the prior on the weights and biases to be the standard normal. See Figure 10. There are N data points, D features, and H hidden units.</paragraph_1> | diagram | 0.996771 | |||
OpenReview | ICLR | 2,017 | Neural Program Lattices | We propose the Neural Program Lattice (NPL), a neural network that learns to perform complex tasks by composing low-level programs to express high-level programs. Our starting point is the recent work on Neural Programmer-Interpreters (NPI), which can only learn from strong supervision that contains the whole hierarchy... | Deep learning, Semi-Supervised Learning | [
7,
4,
7
] | Accept (Poster) | Chengtao Li, Daniel Tarlow, Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman | ctli@mit.edu, dtarlow@microsoft.com, algaunt@microsoft.com, mabrocks@microsoft.com, nkushman@microsoft.com | 20161104 | https://openreview.net/forum?id=HJjiFK5gx | HJjiFK5gx | @inproceedings{
li2017neural,
title={Neural Program Lattices},
author={Chengtao Li and Daniel Tarlow and Alexander L. Gaunt and Marc Brockschmidt and Nate Kushman},
booktitle={International Conference on Learning Representations},
year={2017},
url={https://openreview.net/forum?id=HJjiFK5gx}
} | OpenReview/ICLR/figures/2017/accept_poster/HJjiFK5gx/Figure1.png | 1 | Figure 1: Stack-based NPI: Four time steps from the execution of the stack-based NPI model. Each color/hash pattern represents a unique set of unchanging data values which, over time, move up and down (and in and out of) the stack. Operations below the dotted line to calculate the new world state are executed only at t... | <paragraph_1>The basic structure of the reformulated model can be seen in Figure 1. The model learns a library of programs, G, and arguments, R, to these programs, where each program g ∈Rn and each argument</paragraph_1>
<paragraph_2>An LSTM-based controller, shown in Figure 2, is used to generate the sequence of actio... | diagram | 0.85818 | ||
OpenReview | ICLR | 2,017 | Transfer of View-manifold Learning to Similarity Perception of Novel Objects | We develop a model of perceptual similarity judgment based on re-training a deep convolution neural network (DCNN) that learns to associate different views of each 3D object to capture the notion of object persistence and continuity in our visual experience. The re-training process effectively performs distance metric ... | Deep learning, Transfer Learning | DCNN trained with multiple views of the same object can develop human-like perpetual similarity judgment that can transfer to novel objects | [
6,
5,
7
] | Accept (Poster) | Xingyu Lin, Hao Wang, Zhihao Li, Yimeng Zhang, Alan Yuille, Tai Sing Lee | sean.linxingyu@pku.edu.cn, hao.wang@pku.edu.cn, zhihaol@andrew.cmu.edu, yimengzh@andrew.cmu.edu, alan.yuille@jhu.edu, tai@cnbc.cmu.edu | 20161105 | https://openreview.net/forum?id=B1gtu5ilg | B1gtu5ilg | @inproceedings{
lin2017transfer,
title={Transfer of View-manifold Learning to Similarity Perception of Novel Objects},
author={Xingyu Lin and Hao Wang and Zhihao Li and Yimeng Zhang and Alan Yuille and Tai Sing Lee},
booktitle={International Conference on Learning Representations},
year={2017},
url={https://openreview.... | OpenReview/ICLR/figures/2017/accept_poster/B1gtu5ilg/Figure4.png | 4 | Figure 4: Hierarchical clustering of the alien objects, based on (a) human perceptions, (b)A lexNet features and (c) OPnet features. The dendrograms illustrate how each cluster is composed by drawing a U-shaped link between a cluster and its children. The height of each U-link denotes the distance between its children ... | <paragraph_1>Using the novel objects from Tenenbaum et al. (2011), we are able to compare our networks with human similarity perception. We collect 41 images from the paper, one image per object. A pairwise similarity matrix is calculated based on the cosine distance of their feature representations. We can then perfor... | diagram | 0.958847 | |
OpenReview | ICLR | 2,017 | End-to-end Optimized Image Compression | We describe an image compression method, consisting of a nonlinear analysis transformation, a uniform quantizer, and a nonlinear synthesis transformation. The transforms are constructed in three successive stages of convolutional linear filters and nonlinear activation functions. Unlike most convolutional neural networ... | [
8,
8,
7,
8,
9
] | Accept (Oral) | Johannes Ballé, Valero Laparra, Eero P. Simoncelli | johannes.balle@nyu.edu, valero.laparra@uv.es, eero.simoncelli@nyu.edu | 20161105 | https://openreview.net/forum?id=rJxdQ3jeg | rJxdQ3jeg | @inproceedings{
ball{\'e}2017endtoend,
title={End-to-end Optimized Image Compression},
author={Johannes Ball{\'e} and Valero Laparra and Eero P. Simoncelli},
booktitle={International Conference on Learning Representations},
year={2017},
url={https://openreview.net/forum?id=rJxdQ3jeg}
} | OpenReview/ICLR/figures/2017/accept_oral/rJxdQ3jeg/Figure1.png | 1 | Figure 1: General nonlinear transform coding framework (Ballé, Laparra, and Simoncelli, 2016). A vector of image intensities x ∈ RN is mapped to a latent code space via a parametric analysis transform, y = ga(x;φ). This representation is quantized, yielding a discrete-valued vector q ∈ ZM which is then compressed. The... | diagram | 0.989504 | ||||
OpenReview | ICLR | 2,017 | Neural Architecture Search with Reinforcement Learning | Neural networks are powerful and flexible models that work well for many difficult learning tasks in image, speech and natural language understanding. Despite their success, neural networks are still hard to design. In this paper, we use a recurrent network to generate the model descriptions of neural networks and trai... | [
9,
9,
9
] | Accept (Oral) | Barret Zoph, Quoc Le | barretzoph@google.com, qvl@google.com | 20161104 | https://openreview.net/forum?id=r1Ue8Hcxg | r1Ue8Hcxg | @inproceedings{
zoph2017neural,
title={Neural Architecture Search with Reinforcement Learning},
author={Barret Zoph and Quoc Le},
booktitle={International Conference on Learning Representations},
year={2017},
url={https://openreview.net/forum?id=r1Ue8Hcxg}
} | OpenReview/ICLR/figures/2017/accept_oral/r1Ue8Hcxg/Figure5.png | 5 | Figure 5: An example of a recurrent cell constructed from a tree that has two leaf nodes (base 2) and one internal node. Left: the tree that defines the computation steps to be predicted by controller. Center: an example set of predictions made by the controller for each computation step in the tree. Right: the computa... | <paragraph_1>To make this process more clear, we show an example in Figure 5, for a tree structure that has two leaf nodes and one internal node. The leaf nodes are indexed by 0 and 1, and the internal node is indexed by 2. The controller RNN needs to first predict 3 blocks, each block specifying a combination method an... | diagram | 0.96554 | |||
OpenReview | ICLR | 2,018 | On Unifying Deep Generative Models | Deep generative models have achieved impressive success in recent years. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as powerful frameworks for deep generative model learning, have largely been considered as two distinct paradigms and received extensive independent studies respectively. ... | deep generative models, generative adversarial networks, variational autoencoders, variational inference | A unified statistical view of the broad class of deep generative models | [
6,
7,
7
] | Accept (Poster) | Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Eric P. Xing | zhitinghu@gmail.com, yangtze2301@gmail.com, rsalakhu@cs.cmu.edu, epxing@cs.cmu.edu | 20171027 | https://openreview.net/forum?id=rylSzl-R- | rylSzl-R- | @inproceedings{
hu2018on,
title={On Unifying Deep Generative Models},
author={Zhiting Hu and Zichao Yang and Ruslan Salakhutdinov and Eric P. Xing},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=rylSzl-R-},
} | OpenReview/ICLR/figures/2018/accept_poster/rylSzl-R-/Figure1.png | 1 | Figure 1: (a) Conventional view of ADA. To make direct correspondence to GANs, we use z to denote the data and x the feature. Subscripts src and tgt denote source and target domains, respectively. (b) Conventional view of GANs. (c) Schematic graphical model of both ADA and GANs (Eq.3). Arrows with solid lines denote ge... | <paragraph_1>We first review the conventional formulation of ADA. Figure 1(a) illustrates the computation flow. Let z be a data example either in the source or target domain, and y ∈{0, 1} the domain indicator with y = 0 indicating the target domain and y = 1 the source domain. The data distributions conditioning on the ... | diagram | 0.997632 | |
OpenReview | ICLR | 2,018 | Communication Algorithms via Deep Learning | Coding theory is a central discipline underpinning wireline and wireless modems that are the workhorses of the information age. Progress in coding theory is largely driven by individual human ingenuity with sporadic breakthroughs over the past century. In this paper we study whether it is possible to automate the disco... | coding theory, recurrent neural network, communication | We show that creatively designed and trained RNN architectures can decode well known sequential codes and achieve close to optimal performances. | [
6,
2,
9
] | Accept (Poster) | Hyeji Kim, Yihan Jiang, Ranvir B. Rana, Sreeram Kannan, Sewoong Oh, Pramod Viswanath | hyejikim@illinois.edu, yihanrogerjiang@gmail.com, rbrana2@illinois.edu, ksreeram@uw.edu, sewoong79@gmail.com, pramodv@illinois.edu | 20171027 | https://openreview.net/forum?id=ryazCMbR- | ryazCMbR- | @inproceedings{
kim2018communication,
title={Communication Algorithms via Deep Learning},
author={Hyeji Kim and Yihan Jiang and Ranvir B. Rana and Sreeram Kannan and Sewoong Oh and Pramod Viswanath},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=ryaz... | OpenReview/ICLR/figures/2018/accept_poster/ryazCMbR-/Figure12.png | 12 | Figure 12: rate-1/3 turbo encoder (top) and neural turbo decoder N-Turbo (bottom) | <paragraph_1>in Figure 12. Two identical rate-1/2 RSC encoders are used, encoder 1 with original sequence b as input and encoder 2 with a randomly permuted version of b as input. Interleaver performs the random permutation. As the first output sequence c1(1) of encoder 1 is identical to the output sequence c1(2) of enco... | diagram | 0.988866 | |
OpenReview | ICLR | 2,018 | DORA The Explorer: Directed Outreaching Reinforcement Action-Selection | Exploration is a fundamental aspect of Reinforcement Learning, typically implemented using stochastic action-selection. Exploration, however, can be more efficient if directed toward gaining new world knowledge. Visit-counters have been proven useful both in practice and in theory for directed exploration. However, a m... | Reinforcement Learning, Exploration, Model-Free | We propose a generalization of visit-counters that evaluate the propagating exploratory value over trajectories, enabling efficient exploration for model-free RL | [
6,
6,
7
] | Accept (Poster) | Lior Fox, Leshem Choshen, Yonatan Loewenstein | lior.fox@mail.huji.ac.il, leshem.choshen@mail.huji.ac.il, yonatan.loewenstein@mail.huji.ac.il | 20171027 | https://openreview.net/forum?id=ry1arUgCW | ry1arUgCW | @inproceedings{
fox2018dora,
title={{DORA} The Explorer: Directed Outreaching Reinforcement Action-Selection},
author={Lior Fox and Leshem Choshen and Yonatan Loewenstein},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=ry1arUgCW},
} | OpenReview/ICLR/figures/2018/accept_poster/ry1arUgCW/Figure2.png | 2 | Figure 2: Bridge MDP | <paragraph_1>To demonstrate the advantage of using E-values over standard counters, we tested an ϵ-greedy agent with an exploration bonus of 1 log1−α E added to the observed reward on the bridge MDP (Figure 2). To measure the learning progress and its convergence, we calculated the mean square error</paragraph_1>
<para... | diagram | 0.865206 | |
OpenReview | ICLR | 2,018 | Stochastic Variational Video Prediction | Predicting the future in real-world settings, particularly from raw sensory observations such as images, is exceptionally challenging. Real-world events can be stochastic and unpredictable, and the high dimensionality and complexity of natural images requires the predictive model to build an intricate understanding of ... | video prediction, stochastic prediction, variational inference, unsupervised learning | Stochastic variational video prediction in real-world settings. | [
7,
7,
7
] | Accept (Poster) | Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H. Campbell, Sergey Levine | mb2@uiuc.edu, cbfinn@eecs.berkeley.edu, dumitru@google.com, rhc@illinois.edu, svlevine@eecs.berkeley.edu | 20171027 | https://openreview.net/forum?id=rk49Mg-CW | rk49Mg-CW | @inproceedings{
babaeizadeh2018stochastic,
title={Stochastic Variational Video Prediction},
author={Mohammad Babaeizadeh and Chelsea Finn and Dumitru Erhan and Roy H. Campbell and Sergey Levine},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=rk49Mg-C... | OpenReview/ICLR/figures/2018/accept_poster/rk49Mg-CW/Figure2.png | 2 | Figure 2: Probabilistic graphical model of stochastic variational video prediction, assuming time-invariant latent. The generative model predicts the next frame conditioned on the previous frames and latent variables (solid lines), while the variational inference model approximates the posterior given all the frames (d... | <paragraph_1>In order to construct our stochastic variational video prediction model, we first formulate a probabilistic graphical model that explains the stochasticity in the video. Since our goal is to perform conditional video prediction, the predictions are conditioned on a set of c context frames x0, . . . , xc−1 (... | diagram | 0.980932 | |
OpenReview | ICLR | 2,018 | Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks | For every prediction we might wish to make, we must decide what to observe (what source of information) and when to observe it. Because making observations is costly, this decision must trade off the value of information against the cost of observation. Making observations (sensing) should be an active choice. To solve... | Active Sensing, Timely Prediction, Irregular Sampling, Missing Data | [
7,
8,
6
] | Accept (Poster) | Jinsung Yoon, William R. Zame, Mihaela van der Schaar | jsyoon0823@gmail.com, zame@econ.ucla.edu, mihaela.vanderschaar@oxford-man.ox.ac.uk | 20171027 | https://openreview.net/forum?id=r1SnX5xCb | r1SnX5xCb | @inproceedings{
yoon2018deep,
title={Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks},
author={Jinsung Yoon and William R. Zame and Mihaela van der Schaar},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=r1SnX5xCb},
} | OpenReview/ICLR/figures/2018/accept_poster/r1SnX5xCb/Figure3.png | 3 | Figure 3: Diagram of the neural networks for M-RNN | <paragraph_1>avoids overfitting and leads to significant performance improvements as compared to a standard Bi-RNN. (See the Interpolation part of Fig. 3.)</paragraph_1>
<paragraph_2>Imputation: The objective of the imputation block is to construct an imputation function Ψ that operates across streams. Again, we abuse no... | diagram | 0.994405 | ||
OpenReview | ICLR | 2,018 | Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis | We present a real-time method for synthesizing highly complex human motions using a novel training regime we call the auto-conditioned Recurrent Neural Network (acRNN). Recently, researchers have attempted to synthesize new motion by using autoregressive techniques, but existing methods tend to freeze or diverge after ... | motion synthesis, motion prediction, human pose, human motion, recurrent networks, lstm | Synthesize complex and extended human motions using an auto-conditioned LSTM network | [
7,
6,
7
] | Accept (Poster) | Yi Zhou, Zimo Li, Shuangjiu Xiao, Chong He, Zeng Huang, Hao Li | zhou859@usc.edu, zimoli@usc.edu, xsjiu99@sjtu.edu.cn, sal@sjtu.edu.cn, zenghuang@usc.edu, hao@hao-li.com | 20171027 | https://openreview.net/forum?id=r11Q2SlRW | r11Q2SlRW | @inproceedings{
zhou2018autoconditioned,
title={Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis},
author={Yi Zhou and Zimo Li and Shuangjiu Xiao and Chong He and Zeng Huang and Hao Li},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview... | OpenReview/ICLR/figures/2018/accept_poster/r11Q2SlRW/Figure1.png | 1 | Figure 1: Visual diagram of an unrolled Auto-Conditioned RNN (right) with condition length v = 4 and ground truth length u = 4. It is the input at time step t. St is the hidden state. Ot is the output. | <paragraph_1>The acRNN, on the other hand, deals with poor network output explicitly by using it during training. Instead of only feeding in ground-truth instances, we use subsequences of the network’s own outputs at periodic intervals. For instance, sticking with the example above, instead of conditioning the network ... | diagram | 0.999305 | |
OpenReview | ICLR | 2,018 | Quantitatively Evaluating GANs With Divergences Proposed for Training | Generative adversarial networks (GANs) have been extremely effective in approximating complex distributions of high-dimensional, input data samples, and substantial progress has been made in understanding and improving GAN performance in terms of both theory and application.
However, we currently lack quantitative met... | Generative adversarial networks | An empirical evaluation on generative adversarial networks | [
7,
7,
4
] | Accept (Poster) | Daniel Jiwoong Im, He Ma, Graham W. Taylor, Kristin Branson | daniel.im@aifounded.com, hma02@uoguelph.ca, gwtaylor@uoguelph.ca, kristinbranson@gmail.com | 20171027 | https://openreview.net/forum?id=SJQHjzZ0- | SJQHjzZ0- | @inproceedings{
jiwoong2018quantitatively,
title={Quantitatively Evaluating {GAN}s With Divergences Proposed for Training},
author={Daniel Jiwoong Im and Alllan He Ma and Graham W. Taylor and Kristin Branson},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/for... | OpenReview/ICLR/figures/2018/accept_poster/SJQHjzZ0-/Figure9.png | 9 | Figure 9: GAN Topology for MNIST. | diagram | 0.975679 | ||
OpenReview | ICLR | 2,018 | Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning | Learning policies for complex tasks that require multiple different skills is a major challenge in reinforcement learning (RL). It is also a requirement for its deployment in real-world scenarios. This paper proposes a novel framework for efficient multi-task reinforcement learning. Our framework trains agents to emplo... | Hierarchical Policy, Interpretable Policy, Deep Reinforcement Learning, Multi-task Reinforcement Learning, Skill Acquisition, Language Grounding | A novel hierarchical policy network which can reuse previously learned skills alongside and as subcomponents of new skills by discovering the underlying relations between skills. | [
6,
6,
6
] | Accept (Poster) | Tianmin Shu, Caiming Xiong, Richard Socher | tianmin.shu@ucla.edu, cxiong@salesforce.com, richard@socher.org | 20171027 | https://openreview.net/forum?id=SJJQVZW0b | SJJQVZW0b | @inproceedings{
shu2018hierarchical,
title={Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning},
author={Tianmin Shu and Caiming Xiong and Richard Socher},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=SJJQVZW0b},
} | OpenReview/ICLR/figures/2018/accept_poster/SJJQVZW0b/Figure7.png | 7 | Figure 7: Hierarchical plans for “Put x on y” tasks. Top: an example of performing trained tasks; bottom: an example of generalizing the plan composition to unseen tasks. | <paragraph_1>We visualize typical hierarchical plans of several tasks generated by global policies learned by our full model in Appendix C (Figure 6 and Figure 7)1. It can been seen from the examples that our global policies adjust the composed plans in different scenarios. For instance, in the second plan on the first ... | diagram | 0.932981 | |
OpenReview | ICLR | 2,018 | Compositional Attention Networks for Machine Reasoning | We present Compositional Attention Networks, a novel fully differentiable neural network architecture, designed to facilitate explicit and expressive reasoning. While many types of neural networks are effective at learning and generalizing from massive quantities of data, this model moves away from monolithic black-box... | Deep Learning, Reasoning, Memory, Attention, VQA, CLEVR, Recurrent Neural Networks, Module Networks, Compositionality | We present a novel architecture, based on dynamic memory, attention and composition for the task of machine reasoning. | [
7,
6,
7
] | Accept (Poster) | Drew A. Hudson, Christopher D. Manning | dorarad@cs.stanford.edu, manning@cs.stanford.edu | 20171027 | https://openreview.net/forum?id=S1Euwz-Rb | S1Euwz-Rb | @inproceedings{
arad2018compositional,
title={Compositional Attention Networks for Machine Reasoning},
author={Drew Arad Hudson and Christopher D. Manning},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=S1Euwz-Rb},
} | OpenReview/ICLR/figures/2018/accept_poster/S1Euwz-Rb/Figure4.png | 4 | Figure 4: The Read Unit (RU) diagram. Blue refers to control flow, purple to knowledge flow and red to memory flow. See section 3.2.2 for description. | <paragraph_1>The Read Unit is provided with access to the knowledge base KBV , along with the previous memory state mi−1 and the current control ci. It is responsible for retrieving relevant content from the Knowledge Base KBV for the reasoning task that the MAC cell should accomplish at this step, which is represented... | diagram | 0.999744 | |
OpenReview | ICLR | 2,018 | DCN+: Mixed Objective And Deep Residual Coattention for Question Answering | Traditional models for question answering optimize using cross entropy loss, which encourages exact answers at the cost of penalizing nearby or overlapping answers that are sometimes equally accurate. We propose a mixed objective that combines cross entropy loss with self-critical policy learning, using rewards derived... | question answering, deep learning, natural language processing, reinforcement learning | We introduce the DCN+ with deep residual coattention and mixed-objective RL, which achieves state of the art performance on the Stanford Question Answering Dataset. | [
6,
8,
7
] | Accept (Poster) | Caiming Xiong, Victor Zhong, Richard Socher | cxiong@salesforce.com, richard@socher.org, victor@victorzhong.com | 20171027 | https://openreview.net/forum?id=H1meywxRW | H1meywxRW | @inproceedings{
xiong2018dcn,
title={{DCN}+: Mixed Objective And Deep Residual Coattention for Question Answering},
author={Caiming Xiong and Victor Zhong and Richard Socher},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=H1meywxRW},
} | OpenReview/ICLR/figures/2018/accept_poster/H1meywxRW/Figure1.png | 1 | Figure 1: Deep residual coattention encoder. | <paragraph_1>Because it only has a single-layer coattention encoder, the DCN is limited in its ability to compose complex input representations. Vaswani et al. (2017) proposed stacked self-attention modules to facilitate signal traversal. They also showed that the network’s ability to model long-range dependencies can ... | diagram | 0.867131 | |
OpenReview | ICLR | 2,018 | Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling | Recurrent neural networks (RNN), convolutional neural networks (CNN) and self-attention networks (SAN) are commonly used to produce context-aware representations. RNN can capture long-range dependency but is hard to parallelize and not time-efficient. CNN focuses on local dependency but does not perform well on some ta... | deep learning, attention mechanism, sequence modeling, natural language processing, sentence embedding | A self-attention network for RNN/CNN-free sequence encoding with small memory consumption, highly parallelizable computation and state-of-the-art performance on several NLP tasks | [
6,
6,
9
] | Accept (Poster) | Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang | tao.shen@student.uts.edu.au, tianyizh@uw.edu, guodong.long@uts.edu.au, jing.jiang@uts.edu.au, chengqi.zhang@uts.edu.au | 20171027 | https://openreview.net/forum?id=H1cWzoxA- | H1cWzoxA- | @inproceedings{
shen2018bidirectional,
title={Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling},
author={Tao Shen and Tianyi Zhou and Guodong Long and Jing Jiang and Chengqi Zhang},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.... | OpenReview/ICLR/figures/2018/accept_poster/H1cWzoxA-/Figure2.png | 2 | Figure 2: Masked self-attention mechanism. fij denotes f(xi, xj) in Eq.(9). | <paragraph_1>where W (1) ∈Rde×de, W (2) ∈Rde×dq. The procedures to calculate the attention output from f(xi, xj) are identical to those in token2token self-attention. We use s = gm(x, M) to denote the complete process of masked self-attention with s = [s1, s2, . . . , sn] as the output sequence. An illustration of mask... | diagram | 0.991336 | |
OpenReview | ICLR | 2,018 | Active Learning for Convolutional Neural Networks: A Core-Set Approach | Convolutional neural networks (CNNs) have been successfully applied to many recognition and learning tasks using a universal recipe; training a deep model on a very large dataset of supervised examples. However, this approach is rather restrictive in practice since collecting a large set of labeled images is very expe... | Active Learning, Convolutional Neural Networks, Core-Set Selection | We approach to the problem of active learning as a core-set selection problem and show that this approach is especially useful in the batch active learning setting which is crucial when training CNNs. | [
7,
7,
7
] | Accept (Poster) | Ozan Sener, Silvio Savarese | ozansener@cs.stanford.edu, ssilvio@stanford.edu | 20171027 | https://openreview.net/forum?id=H1aIuk-RW | H1aIuk-RW | @inproceedings{
sener2018active,
title={Active Learning for Convolutional Neural Networks: A Core-Set Approach},
author={Ozan Sener and Silvio Savarese},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=H1aIuk-RW},
} | OpenReview/ICLR/figures/2018/accept_poster/H1aIuk-RW/Figure1.png | 1 | Figure 1: Visualization of the Theorem 1. Consider the set of selected points s and the points in the remainder of the dataset [n] \ s, our results shows that if s is the δs cover of the dataset,∣∣∣ 1 n ∑ i∈[n] l(xi, yi, As)− 1 |s| ∑ j∈s l(xj , yj ;As) ∣∣∣ ≤ O (δs) +O (√ 1 n ) | <paragraph_1>n P i∈[n] l(xi, yi; As). We state the theorem in this form to be consistent with (3). We visualize this theorem in Figure 1 and defer its proof to the appendix. In this theorem, “a set s is a δ cover of a set s⋆” means a set of balls with radius δ centered at each member of s can cover the entire s⋆. Infor... | diagram | 0.917242 | |
OpenReview | ICLR | 2,018 | Interactive Grounded Language Acquisition and Generalization in a 2D World | We build a virtual agent for learning language in a 2D maze-like world. The agent sees images of the surrounding environment, listens to a virtual teacher, and takes actions to receive rewards. It interactively learns the teacher’s language from scratch based on two language use cases: sentence-directed navigation and ... | grounded language learning and generalization, zero-shot language learning | Training an agent in a 2D virtual world for grounded language acquisition and generalization. | [
6,
7,
6
] | Accept (Poster) | Haonan Yu, Haichao Zhang, Wei Xu | haonanyu@baidu.com, zhanghaichao@baidu.com, wei.xu@baidu.com | 20171027 | https://openreview.net/forum?id=H1UOm4gA- | H1UOm4gA- | @inproceedings{
yu2018interactive,
title={Interactive Grounded Language Acquisition and Generalization in a 2D World},
author={Haonan Yu and Haichao Zhang and Wei Xu},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=H1UOm4gA-},
} | OpenReview/ICLR/figures/2018/accept_poster/H1UOm4gA-/Figure17.png | 17 | Figure 17: An overview of the baseline VL. The computations of NAV and QA only differ in the last MLPs. | <paragraph_1>VIS-LSTM [VL] An adaptation of a model devised by Ren et al. (2015) which was originally proposed for VQA. We flatten h and project it to the word embedding space RD. Then it is appended to the input sentence s as the first word. The augmented sentence goes through an LSTM whose last state is used for both N... | diagram | 0.940663 | |
OpenReview | ICLR | 2,018 | Deep Complex Networks | At present, the vast majority of building blocks, techniques, and architectures for deep learning are based on real-valued operations and representations. However, recent work on recurrent neural networks and older fundamental theoretical analysis suggests that complex numbers could have a richer representational capac... | deep learning, complex-valued neural networks | [
8,
4,
7
] | Accept (Poster) | Chiheb Trabelsi, Olexa Bilaniuk, Ying Zhang, Dmitriy Serdyuk, Sandeep Subramanian, Joao Felipe Santos, Soroush Mehri, Negar Rostamzadeh, Yoshua Bengio, Christopher J Pal | chiheb.trabelsi@polymtl.ca, olexa.bilaniuk@umontreal.ca, ying.zhang@umontreal.ca, serdyuk@iro.umontreal.ca, sandeep.subramanian.1@umontreal.ca, jfsantos@emt.inrs.ca, soroush.mehri@microsoft.com, negar@elementai.com, yoshua.bengio@umontreal.ca, christopher.pal@polymtl.ca | 20171027 | https://openreview.net/forum?id=H1T2hmZAb | H1T2hmZAb | @inproceedings{
trabelsi2018deep,
title={Deep Complex Networks},
author={Chiheb Trabelsi and Olexa Bilaniuk and Ying Zhang and Dmitriy Serdyuk and Sandeep Subramanian and Joao Felipe Santos and Soroush Mehri and Negar Rostamzadeh and Yoshua Bengio and Christopher J Pal},
booktitle={International Conference on Learning ... | OpenReview/ICLR/figures/2018/accept_poster/H1T2hmZAb/Figure1.png | 1 | Figure 1: Complex convolution and residual network implementation details. | <paragraph_1>As illustrated in Figure 1a, if we use matrix notation to represent real and imaginary parts of the convolution operation we have: ℜ(W ∗h) ℑ(W ∗h)</paragraph_1>
<paragraph_2>A deep convolutional residual network of the nature presented in He et al. (2015a; 2016) consists of 3 stages within which feature ... | diagram | 0.971942 | ||
OpenReview | ICLR | 2,018 | Few-Shot Learning with Graph Neural Networks | We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not. By assimilating generic message-passing inference algorithms with their neural-network counterparts, we defin... | [
7,
7,
7
] | Accept (Poster) | Victor Garcia Satorras, Joan Bruna Estrach | vgsatorras@gmail.com, bruna@cims.nyu.edu | 20171027 | https://openreview.net/forum?id=BJj6qGbRW | BJj6qGbRW | @inproceedings{
garcia2018fewshot,
title={Few-Shot Learning with Graph Neural Networks},
author={Victor Garcia Satorras and Joan Bruna Estrach},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=BJj6qGbRW},
} | OpenReview/ICLR/figures/2018/accept_poster/BJj6qGbRW/Figure1.png | 1 | Figure 1: Visual representation of One-Shot Learning setting. | diagram | 0.989425 | ||||
OpenReview | ICLR | 2,018 | A Simple Neural Attentive Meta-Learner | Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task. In response, recent work in meta-learning proposes training a meta-learner on a distribution of similar tasks, in the hopes of generalization to novel but... | meta-learning, few-shot learning | a simple RNN-based meta-learner that achieves SOTA performance on popular benchmarks | [
6,
7,
6
] | Accept (Poster) | Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, Pieter Abbeel | nmishra@berkeley.edu, rohaninejadm@berkeley.edu, adslcx@gmail.com, pabbeel@gmail.com | 20171027 | https://openreview.net/forum?id=B1DmUzWAW | B1DmUzWAW | @inproceedings{
mishra2018a,
title={A Simple Neural Attentive Meta-Learner},
author={Nikhil Mishra and Mostafa Rohaninejad and Xi Chen and Pieter Abbeel},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=B1DmUzWAW},
} | OpenReview/ICLR/figures/2018/accept_poster/B1DmUzWAW/Figure1.png | 1 | Figure 1: Overview of our simple neural attentive learner (SNAIL); in this example, two blocks of TC layers (orange) are interleaved with two causal attention layers (green). The same class of model architectures can be applied to both supervised and reinforcement learning. | <paragraph_1>Despite their individual shortcomings, temporal convolutions and attention complement each other: while the former provide high-bandwidth access at the expense of finite context size, the latter provide pinpoint access over an infinitely large context. Hence, we construct SNAIL by combining the two: we use t... | diagram | 0.87452 | |
OpenReview | ICLR | 2,018 | Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions | The driving force behind deep networks is their ability to compactly represent rich classes of functions. The primary notion for formally reasoning about this phenomenon is expressive efficiency, which refers to a situation where one network must grow unfeasibly large in order to replicate functions of another. To date... | Deep Learning, Expressive Efficiency, Dilated Convolutions, Tensor Decompositions | We introduce the notion of mixed tensor decompositions, and use it to prove that interconnecting dilated convolutional networks boosts their expressive power. | [
9,
8,
7
] | Accept (Oral) | Nadav Cohen, Ronen Tamari, Amnon Shashua | cohennadav@ias.edu, ronent@cs.huji.ac.il, shashua@cs.huji.ac.il | 20171024 | https://openreview.net/forum?id=S1JHhv6TW | S1JHhv6TW | @inproceedings{
cohen2018boosting,
title={Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions},
author={Nadav Cohen and Ronen Tamari and Amnon Shashua},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=S1JHhv6TW},
} | OpenReview/ICLR/figures/2018/accept_oral/S1JHhv6TW/Figure4.png | 4 | Figure 4: Best viewed in color. (a) Two mode trees T and T̄ along with a possible choice of mixture nodes (same as in fig. 3(a)). (b) Sample of the resulting hybrid mode trees (def. 2). | diagram | 0.96057 | ||
OpenReview | ICLR | 2,019 | Deep Layers as Stochastic Solvers | We provide a novel perspective on the forward pass through a block of layers in a deep network. In particular, we show that a forward pass through a standard dropout layer followed by a linear layer and a non-linear activation is equivalent to optimizing a convex objective with a single iteration of a $\tau$-nice Proxi... | deep networks, optimization | A framework that links deep network layers to stochastic optimization algorithms; can be used to improve model accuracy and inform network design. | [
8,
7,
7
] | Accept (Poster) | Adel Bibi, Bernard Ghanem, Vladlen Koltun, Rene Ranftl | adel.bibi@kaust.edu.sa, bernard.ghanem@kaust.edu.sa, vkoltun@gmail.com, ranftlr@gmail.com | 20180927 | https://openreview.net/forum?id=ryxxCiRqYX | ryxxCiRqYX | @inproceedings{
bibi2018deep,
title={Deep Layers as Stochastic Solvers},
author={Adel Bibi and Bernard Ghanem and Vladlen Koltun and Rene Ranftl},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=ryxxCiRqYX},
} | OpenReview/ICLR/figures/2019/accept_poster/ryxxCiRqYX/Figure1.png | 1 | Figure 1: An overview of the tight relation between a single iteration of a stochastic solver and the forward pass through the lth layer in a network that consists of dropout followed by a linear transformation and a non-linear activation. We study an instance of problem (1) with quadratic F (x), where xl−1 are the inp... | <paragraph_1>This section is organized as follows. We introduce our notation and preliminaries in Section 3.1. In Section 3.2, we present a motivational example relating a single iteration of proximal gradient descent (Prox-GD) on (1) to the forward pass through a fully-connected layer followed by a nonlinear activatio... | diagram | 0.997574 | |
OpenReview | ICLR | 2,019 | Learning Factorized Multimodal Representations | Learning multimodal representations is a fundamentally complex research problem due to the presence of multiple heterogeneous sources of information. Although the presence of multiple modalities provides additional valuable information, there are two key challenges to address when learning from multimodal data: 1) mode... | multimodal learning, representation learning | We propose a model to learn factorized multimodal representations that are discriminative, generative, and interpretable. | [
6,
7,
7
] | Accept (Poster) | Yao-Hung Hubert Tsai, Paul Pu Liang, Amir Zadeh, Louis-Philippe Morency, Ruslan Salakhutdinov | yaohungt@cs.cmu.edu, pliang@cs.cmu.edu, abagherz@cs.cmu.edu, morency@cs.cmu.edu, rsalakhu@cs.cmu.edu | 20180927 | https://openreview.net/forum?id=rygqqsA9KX | rygqqsA9KX | @inproceedings{
tsai2018learning,
title={Learning Factorized Multimodal Representations},
author={Yao-Hung Hubert Tsai and Paul Pu Liang and Amir Zadeh and Louis-Philippe Morency and Ruslan Salakhutdinov},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?i... | OpenReview/ICLR/figures/2019/accept_poster/rygqqsA9KX/Figure6.png | 6 | Figure 6: The surrogate inference graphical model to deal with missing modalities in MFM. Red lines denote original inference in MFM and green lines denote surrogate inference to infer latent codes given present modalities. | <paragraph_1>We illustrate the surrogate inference for addressing the missing modalities issue in Figure 6. The surrogate inference model infers the latent codes given the present modalities. These inferred latent codes can then be used for reconstructing the missing modalities or label prediction in the presence of mi... | diagram | 0.96599 | |
OpenReview | ICLR | 2,019 | Conditional Network Embeddings | Network Embeddings (NEs) map the nodes of a given network into $d$-dimensional Euclidean space $\mathbb{R}^d$. Ideally, this mapping is such that 'similar' nodes are mapped onto nearby points, such that the NE can be used for purposes such as link prediction (if 'similar' means being 'more likely to be connected') or c... | Network embedding, graph embedding, learning node representations, link prediction, multi-label classification of nodes | We introduce a network embedding method that accounts for prior information about the network, yielding superior empirical performance. | [
5,
6,
4
] | Accept (Poster) | Bo Kang, Jefrey Lijffijt, Tijl De Bie | bo.kang@ugent.be, jefrey.lijffijt@ugent.be, tijl.debie@ugent.be | 20180927 | https://openreview.net/forum?id=ryepUj0qtX | ryepUj0qtX | @inproceedings{
kang2018conditional,
title={Conditional Network Embeddings},
author={Bo Kang and Jefrey Lijffijt and Tijl De Bie},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=ryepUj0qtX},
} | OpenReview/ICLR/figures/2019/accept_poster/ryepUj0qtX/Figure2.png | 2 | Figure 2: The entity relationship diagram of the studentdb dataset. | <paragraph_1>• Facebook (Leskovec & Krevl, 2015): In this network, nodes are the users and links represent the friendships between the users. The network has 4,039 nodes and 88,234 links. • arXiv ASTRO-PH (Leskovec & Krevl, 2015): In this network nodes represent authors of papers submitted to arXiv. The links represent... | diagram | 0.992351 | |
OpenReview | ICLR | 2,019 | DPSNet: End-to-end Deep Plane Sweep Stereo | Multiview stereo aims to reconstruct scene depth from images acquired by a camera under arbitrary motion. Recent methods address this problem through deep learning, which can utilize semantic cues to deal with challenges such as textureless and reflective regions. In this paper, we present a convolutional neural networ... | Deep Learning, Stereo, Depth, Geometry | A convolution neural network for multi-view stereo matching whose design is inspired by best practices of traditional geometry-based approaches | [
6,
6,
7
] | Accept (Poster) | Sunghoon Im, Hae-Gon Jeon, Stephen Lin, In So Kweon | dlarl8927@kaist.ac.kr, haegonj@andrew.cmu.edu, stevelin@microsoft.com, iskweon77@kaist.ac.kr | 20180927 | https://openreview.net/forum?id=ryeYHi0ctQ | ryeYHi0ctQ | @inproceedings{
im2018dpsnet,
title={{DPSN}et: End-to-end Deep Plane Sweep Stereo},
author={Sunghoon Im and Hae-Gon Jeon and Stephen Lin and In So Kweon},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=ryeYHi0ctQ},
} | OpenReview/ICLR/figures/2019/accept_poster/ryeYHi0ctQ/Figure2.png | 2 | Figure 2: Overview of the DPSNet pipeline. | <paragraph_1>Our Deep Plane Sweep Network (DPSNet) is inspired by traditional multiview stereo practices for dense depth estimation and consists of four parts: feature extraction, cost volume generation, cost aggregation and depth map regression. The overall framework is shown in Figure 2.</paragraph_1> | diagram | 0.962483 | |
OpenReview | ICLR | 2,019 | Graph HyperNetworks for Neural Architecture Search | Neural architecture search (NAS) automatically finds the best task-specific neural network topology, outperforming many manual architecture designs. However, it can be prohibitively expensive as the search requires training thousands of different networks, while each training run can last for hours. In this work, we pr... | neural, architecture, search, graph, network, hypernetwork, meta, learning, anytime, prediction | [
7,
6,
7
] | Accept (Poster) | Chris Zhang, Mengye Ren, Raquel Urtasun | cjzhang@edu.uwaterloo.ca, mren@cs.toronto.edu, urtasun@cs.toronto.edu | 20180927 | https://openreview.net/forum?id=rkgW0oA9FX | rkgW0oA9FX | @inproceedings{
zhang2018graph,
title={Graph HyperNetworks for Neural Architecture Search},
author={Chris Zhang and Mengye Ren and Raquel Urtasun},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=rkgW0oA9FX},
} | OpenReview/ICLR/figures/2019/accept_poster/rkgW0oA9FX/Figure7.png | 7 | Figure 7: Best block found for classification | <paragraph_1>Figure 7 shows the best found block in the CIFAR-10 Experiments.</paragraph_1> | diagram | 0.938143 | ||
OpenReview | ICLR | 2,019 | Learning Implicitly Recurrent CNNs Through Parameter Sharing | We introduce a parameter sharing scheme, in which different layers of a convolutional neural network (CNN) are defined by a learned linear combination of parameter tensors from a global bank of templates. Restricting the number of templates yields a flexible hybridization of traditional CNNs and recurrent networks. C... | deep learning, architecture search, computer vision | We propose a method that enables CNN folding to create recurrent connections | [
6,
7,
8
] | Accept (Poster) | Pedro Savarese, Michael Maire | savarese@ttic.edu, mmaire@uchicago.edu | 20180927 | https://openreview.net/forum?id=rJgYxn09Fm | rJgYxn09Fm | @inproceedings{
savarese2018learning,
title={Learning Implicitly Recurrent {CNN}s Through Parameter Sharing},
author={Pedro Savarese and Michael Maire},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=rJgYxn09Fm},
} | OpenReview/ICLR/figures/2019/accept_poster/rJgYxn09Fm/Figure6.png | 6 | Figure 6: SWRN 40-8-8 (8 parameter templates shared among groups of 40−4 3 − 2 = 10 layers) trained with soft parameter sharing on CIFAR-10. Each stage (originally with 12 layers – the first two do not participate in parameter sharing) can be folded to yield blocks with complex recurrences. For clarity, we use colors t... | <paragraph_1>Figure 6 presents an additional example, where non-trivial recurrences (unlike the one in Figure 4) emerge naturally, resulting in a model that is rich in structure.</paragraph_1> | diagram | 0.970068 | |
OpenReview | ICLR | 2,019 | Self-Monitoring Navigation Agent via Auxiliary Progress Estimation | The Vision-and-Language Navigation (VLN) task entails an agent following navigational instruction in photo-realistic unknown environments. This challenging task demands that the agent be aware of which instruction was completed, which instruction is needed next, which way to go, and its navigation progress towards the ... | visual grounding, textual grounding, instruction-following, navigation agent | We propose a self-monitoring agent for the Vision-and-Language Navigation task. | [
7,
6,
8
] | Accept (Poster) | Chih-Yao Ma, Jiasen Lu, Zuxuan Wu, Ghassan AlRegib, Zsolt Kira, Richard Socher, Caiming Xiong | cyma@gatech.edu, jiasenlu@gatech.edu, zxwu@cs.umd.edu, alregib@gatech.edu, zkira@gatech.edu, rsocher@salesforce.com, cxiong@salesforce.com | 20180927 | https://openreview.net/forum?id=r1GAsjC5Fm | r1GAsjC5Fm | @misc{
ma2019selfmonitoring,
title={Self-Monitoring Navigation Agent via Auxiliary Progress Estimation},
author={Chih-Yao Ma and Jiasen Lu and Zuxuan Wu and Ghassan AlRegib and Zsolt Kira and Richard Socher and Caiming Xiong},
year={2019},
url={https://openreview.net/forum?id=r1GAsjC5Fm},
} | OpenReview/ICLR/figures/2019/accept_poster/r1GAsjC5Fm/Figure2.png | 2 | Figure 2: Proposed self-monitoring agent consisting of visual-textual co-grounding, progress monitoring, and action selection modules. Textual grounding: identify which part of the instruction has been completed or ongoing and which part is potentially needed for next action. Visual grounding: summarize the observed su... | <paragraph_1>First, we propose a visual and textual co-grounding model for the vision and language navigation task, as illustrated in Fig. 2. We model the agent with a sequence-to-sequence architecture with attention by using a recurrent neural network. More specifically, we use Long Short Term Memory (LSTM) to carry th... | diagram | 0.967222 | |
OpenReview | ICLR | 2,019 | LEARNING TO PROPAGATE LABELS: TRANSDUCTIVE PROPAGATION NETWORK FOR FEW-SHOT LEARNING | The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class. The recently introduced meta-learning approaches tackle this problem by learning a generic classifier across a large number of multiclass classification tasks and general... | few-shot learning, meta-learning, label propagation, manifold learning | We propose a novel meta-learning framework for transductive inference that classifies the entire test set at once to alleviate the low-data problem. | [
7,
6,
5
] | Accept (Poster) | Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sung Ju Hwang, Yi Yang | csyanbin@gmail.com, juho.lee@stats.ox.ac.uk, mike_seop@aitrics.com, shkim@aitrics.com, eunhoy@kaist.ac.kr, sjhwang82@kaist.ac.kr, yi.yang@uts.edu.au | 20180927 | https://openreview.net/forum?id=SyVuRiC5K7 | SyVuRiC5K7 | @inproceedings{
liu2018learning,
title={{LEARNING} {TO} {PROPAGATE} {LABELS}: {TRANSDUCTIVE} {PROPAGATION} {NETWORK} {FOR} {FEW}-{SHOT} {LEARNING}},
author={Yanbin Liu and Juho Lee and Minseop Park and Saehoon Kim and Eunho Yang and Sungju Hwang and Yi Yang},
booktitle={International Conference on Learning Representati... | OpenReview/ICLR/figures/2019/accept_poster/SyVuRiC5K7/Figure1.png | 1 | Figure 1: A conceptual illustration of our transductive meta-learning framework, where lines between nodes represent graph connections and their colors represent the potential direction of label propagation. The neighborhood graph is episodic-wisely trained for transductive inference. | <paragraph_1>Yet, with the meta-learning by episodic training, we can learn the label propagation network as the query examples sampled from the training set can be used to simulate the real test set for transductive inference. Motivated by this finding, we propose Transductive Propagation Network (TPN) to deal with the... | diagram | 0.955474 | |
OpenReview | ICLR | 2,019 | Learning Programmatically Structured Representations with Perceptor Gradients | We present the perceptor gradients algorithm -- a novel approach to learning symbolic representations based on the idea of decomposing an agent's policy into i) a perceptor network extracting symbols from raw observation data and ii) a task encoding program which maps the input symbols to output actions. We show that t... | representation learning, structured representations, symbols, programs | [
5,
6,
7
] | Accept (Poster) | Svetlin Penkov, Subramanian Ramamoorthy | sv.penkov@gmail.com, s.ramamoorthy@ed.ac.uk | 20180927 | https://openreview.net/forum?id=SJggZnRcFQ | SJggZnRcFQ | @inproceedings{
penkov2018learning,
title={Learning Programmatically Structured Representations with Perceptor Gradients},
author={Svetlin Penkov and Subramanian Ramamoorthy},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=SJggZnRcFQ},
} | OpenReview/ICLR/figures/2019/accept_poster/SJggZnRcFQ/Figure2.png | 2 | Figure 2: A diagram of the cart-pole experimental setup. | <paragraph_1>We first consider the problem of balancing a cart-pole system by learning symbolic representations from the raw image observations. The cart-pole system is well studied in optimal control theory and it is typically balanced with an LQR (Zhou et al., 1996). We exploit this knowledge and set the program ρ to ... | diagram | 0.996308 | ||
OpenReview | ICLR | 2,019 | Improving Sequence-to-Sequence Learning via Optimal Transport | Sequence-to-sequence models are commonly trained via maximum likelihood estimation (MLE). However, standard MLE training considers a word-level objective, predicting the next word given the previous ground-truth partial sentence. This procedure focuses on modeling local syntactic patterns, and may fail to capture long-... | NLP, optimal transport, sequence to sequence, natural language processing | [
5,
7,
6
] | Accept (Poster) | Liqun Chen, Yizhe Zhang, Ruiyi Zhang, Chenyang Tao, Zhe Gan, Haichao Zhang, Bai Li, Dinghan Shen, Changyou Chen, Lawrence Carin | liqun.chen@duke.edu, yizhe.zhang@microsoft.com, rz68@duke.edu, chenyang.tao@duke.edu, zhe.gan@microsoft.com, hczhang1@gmail.com, bai.li@duke.edu, dinghan.shen@duke.edu, cchangyou@gmail.com, lcarin@duke.edu | 20180927 | https://openreview.net/forum?id=S1xtAjR5tX | S1xtAjR5tX | @inproceedings{
chen2018improving,
title={Improving Sequence-to-Sequence Learning via Optimal Transport},
author={Liqun Chen and Yizhe Zhang and Ruiyi Zhang and Chenyang Tao and Zhe Gan and Haichao Zhang and Bai Li and Dinghan Shen and Changyou Chen and Lawrence Carin},
booktitle={International Conference on Learning R... | OpenReview/ICLR/figures/2019/accept_poster/S1xtAjR5tX/Figure2.png | 2 | Figure 2: Schematic computation graph of OT loss. | <paragraph_1>2.2 OPTIMAL TRANSPORT DISTANCE AS A SEQUENCE LEVEL LOSS Figure 2 illustrates how OT is computed to construct the sequence-level loss. Given two sentences, we can construct their word-level or phrase-level embedding matrices S and S′, where S = {zi} is usually recognized as the reference sequence embedding ... | diagram | 0.991626 |
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