paper_id stringlengths 3 14 | source stringclasses 3
values | title stringlengths 0 287 ⌀ | abstract stringlengths 84 3.8k | num_figures int32 0 2 | paper_json stringlengths 1.13k 162k | method_figure imagewidth (px) 246 1.69k ⌀ | result_figure imagewidth (px) 248 1.7k ⌀ |
|---|---|---|---|---|---|---|---|
ICLR2020_10 | AgentReview | SCORING-AGGREGATING-PLANNING: LEARNING TASK-AGNOSTIC PRIORS FROM INTERACTIONS AND SPARSE REWARDS FOR ZERO-SHOT GENERALIZATION | Humans can learn task-agnostic priors from interactive experience and utilize the priors for novel tasks without any finetuning. In this paper, we propose ScoringAggregating-Planning (SAP), a framework that can learn task-agnostic semantics and dynamics priors from arbitrary quality interactions with sparse reward and ... | 2 | {"name": "ICLR2020_10.pdf.pdf", "metadata": {"source": "ICLR", "title": "SCORING-AGGREGATING-PLANNING: LEARNING TASK-AGNOSTIC PRIORS FROM INTERACTIONS AND SPARSE REWARDS FOR ZERO-SHOT GENERALIZATION", "authors": [], "emails": [], "sections": [{"heading": "1 INTRODUCTION", "text": "While deep Reinforcement Learning (RL)... | ||
ICLR2020_101 | AgentReview | Deep convolutional neural networks are known to specialize in distilling compact and robust prior from a large amount of data. We are interested in applying deep networks in the absence of training dataset. In this paper, we introduce deep audio prior (DAP) which leverages the structure of a network and the temporal in... | 2 | {"name": "ICLR2020_101.pdf.pdf", "metadata": {"source": "ICLR", "title": "", "authors": [], "emails": [], "sections": [{"text": "To understand the robustness of the deep audio prior, we construct a benchmark dataset Universal-150 for universal sound source separation with a diverse set of sources. We show superior audi... | |||
ICLR2020_105 | AgentReview | "Reinforcement learning (RL) offers a principled way to achieve the optimal cumulative performance i(...TRUNCATED) | 1 | "{\"name\": \"ICLR2020_105.pdf.pdf\", \"metadata\": {\"source\": \"ICLR\", \"title\": \"\", \"author(...TRUNCATED) | Not supported with pagination yet | ||
ICLR2020_106 | AgentReview | "We study the training process of Deep Neural Networks (DNNs) from the Fourier analysis perspective.(...TRUNCATED) | 1 | "{\"name\": \"ICLR2020_106.pdf.pdf\", \"metadata\": {\"source\": \"ICLR\", \"title\": \"\", \"author(...TRUNCATED) | Not supported with pagination yet | ||
ICLR2020_107 | AgentReview | "In recent years, substantial progress has been made on graph convolutional networks (GCN). In this (...TRUNCATED) | 2 | "{\"name\": \"ICLR2020_107.pdf.pdf\", \"metadata\": {\"source\": \"ICLR\", \"title\": \"\", \"author(...TRUNCATED) | |||
ICLR2020_109 | AgentReview | "This paper presents a generic framework to remedy the misalignment in unsupervised domain adaptatio(...TRUNCATED) | 2 | "{\"name\": \"ICLR2020_109.pdf.pdf\", \"metadata\": {\"source\": \"ICLR\", \"title\": \"\", \"author(...TRUNCATED) | |||
ICLR2020_11 | AgentReview | COUNT-GUIDED WEAKLY SUPERVISED LOCALIZA- | "Weakly supervised localization (WSL) aims at training a model to find the positions of objects by p(...TRUNCATED) | 1 | "{\"name\": \"ICLR2020_11.pdf.pdf\", \"metadata\": {\"source\": \"ICLR\", \"title\": \"COUNT-GUIDED (...TRUNCATED) | Not supported with pagination yet | |
ICLR2020_110 | AgentReview | "In many machine learning applications, we are faced with incomplete datasets. In the literature, mi(...TRUNCATED) | 1 | "{\"name\": \"ICLR2020_110.pdf.pdf\", \"metadata\": {\"source\": \"ICLR\", \"title\": \"\", \"author(...TRUNCATED) | Not supported with pagination yet | ||
ICLR2020_111 | AgentReview | "A key feature of intelligent behavior is the ability to learn abstract strategies that transfer to (...TRUNCATED) | 2 | "{\"name\": \"ICLR2020_111.pdf.pdf\", \"metadata\": {\"source\": \"ICLR\", \"title\": \"\", \"author(...TRUNCATED) | |||
ICLR2020_115 | AgentReview | INTRINSIC MUTUAL INFORMATION REWARDS | "Learning useful skills without a manually-designed reward function would have many applications, ye(...TRUNCATED) | 2 | "{\"name\": \"ICLR2020_115.pdf.pdf\", \"metadata\": {\"source\": \"ICLR\", \"title\": \"INTRINSIC MU(...TRUNCATED) |
End of preview. Expand in Data Studio
PaperGuard
A benchmark of academic papers (text + key figures) for evaluating the robustness of multimodal AI peer-review systems.
Schema (single test split)
| column | type | description |
|---|---|---|
paper_id |
string | paper identifier (e.g. 502, ICLR2020_10, 1-26) |
source |
string | one of iclr_2017, AgentReview, F1000 |
title |
string | paper title (may be null) |
abstract |
string | abstract text |
num_figures |
int | number of figures present (0–2) |
paper_json |
string | the full original parsed-paper JSON ({"name", "metadata"}), verbatim |
method_figure |
image | the method figure (<id>-1.png), or null |
result_figure |
image | the result figure (<id>-2.png), or null |
Usage
from datasets import load_dataset
ds = load_dataset("rellabear/PaperGuard", split="test")
row = ds[0]
fig = row["method_figure"] # PIL.Image or None
License & Attribution
Released for non-commercial research under CC-BY-NC-4.0. Please cite the original sources:
- F1000 — via NLPeer (Dycke et al., ACL 2023); F1000Research content is CC-BY.
- iclr_2017 — via PeerRead (Kang et al., NAACL 2018).
- AgentReview — via AgentReview (Jin et al., EMNLP 2024).
ICLR/OpenReview-derived content remains subject to OpenReview's terms.
- Downloads last month
- 39