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MrDhifallah/
CAFF

Name: CAFF
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michiyasunaga/BioLinkBERT-large

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CAFF: Context-Aware Feedback Filtering for Multi-Hop Biomedical Knowledge Graph Evidence Selection

Python

PyTorch

CUDA

Marwan Dhifallah^* . Yu Liu
Dalian University of Technology, Dalian, China
marwan@mail.dlut.edu.cn . yuliu@dlut.edu.cn

TL;DR
The Context Blindness Error
Approach
Repository Structure
Installation
Data
Training
Evaluation
Results
Ablation Study
Configurations
Hyperparameters
Reproducibility
Hardware
Scope and Future Work
Citation
License
Acknowledgements
Contact

TL;DR

CAFF is a triple filter for multi-hop biomedical knowledge graph retrieval-augmented generation. It addresses the Context Blindness Error (CBE): filters that score each candidate triple from (Query, relation, BFS_depth) alone cannot distinguish whether the same triple is relevant or irrelevant under different upstream retained sets. CAFF fixes this with two coupled components:

CSV -- a parameter-free, permutation-invariant summary of the previous hop's retained set.
DBM -- a low-rank, sigmoid-gated dynamic perturbation of the bilinear scoring matrix, generated from the CSV.

Headline results (Orphanet biomedical KG, 3,000 held-out test queries, 3 random seeds, paired bootstrap):

Metric	Mean +/- std
Test F1 (per-hop thresholds)	0.5764 +/- 0.0022
Test F1 (autoregressive)	0.5477 +/- 0.0006
Test MAP	0.6741 +/- 0.0003
Test NDCG@10	0.7090 +/- 0.0003

A complete leave-one-out ablation establishes CSV and DBM as the essential components; two additional losses that were considered during development (a depth-contrastive loss and an HC3-style contrastive loss) were measured and removed because they did not improve held-out F1 on this knowledge graph. See Ablation Study.

The Context Blindness Error

Consider the clinical query:

"What drug targets the pathway of the causal gene of Fanconi anemia complementation group D1?"

The same hop-2 triple <BRCA2, participates_in, HR-repair> is:

Relevant when the hop-1 retained set is {<Fanconi anemia D1, caused_by, BRCA2>}.
Irrelevant when the hop-1 retained set is {<Fanconi anemia D1, caused_by, BRIP1>}.

A filter that sees only (Query, relation, hop) cannot tell these two situations apart and therefore must score the triple identically in both. We call this the Context Blindness Error (CBE). By the Data Processing Inequality, any filter that ignores the previous hop's retained set has expected loss bounded below by I(Y; S_{ell-1} | Q, r, ell), which is strictly positive whenever the retained set carries information about the gold label.

The architectural fix is to feed a summary of S_{ell-1} into the scoring function for hop ell. CAFF does this with CSV (the summary) and DBM (a gating mechanism on the bilinear scorer).

Approach

CAFF operates in four stages during multi-hop evidence retrieval.

Stage 1 -- BFS candidate stratification

A BFS from the query's seed entities collects all triples reachable within L=3 hops, stratified by hop depth. A per-relation frequency cap (K_r=20) prevents any one relation from dominating a candidate set on highly connected entities.

Stage 2 -- Contextual Summary Vector (CSV)

For each hop ell > 1, the retained set from the previous hop is summarized by a parameter-free, permutation-invariant pool over the relation embeddings of its triples:

z_{ell-1} = pool({ E[r] : (h, r, t) in S_{ell-1} })

The default pool is mean. At ell=1 the retained set is empty by convention, so z_0 = 0 and the scorer reduces to its base form.

Stage 3 -- Dynamic Bilinear Modulation (DBM)

The base hop-conditioned bilinear scorer

s_base(Q, r, ell) = Q^T W_ell E[r]

is augmented with a low-rank, context-dependent perturbation generated from z_{ell-1}:

Delta_ell(z) = sigmoid(U z) * (A z) (B z)^T,   with rank rho << d
s_CAFF(Q, r, ell, z) = Q^T (W_ell + Delta_ell(z)) E[r]

This is the only context-aware path in the architecture; Delta_ell adds about 0.8 M parameters at rho=16, d=1024. The sigmoid gate lets DBM smoothly fall back to the base scorer when the context vector is uninformative.

Stage 4 -- Training objective

The training loss is BCE on the per-triple retain/drop label, optionally augmented with two auxiliary losses (a depth-contrastive hinge and an HC3 contrastive loss). Both auxiliaries are exposed as ablation flags. On the held-out Orphanet QA test set, neither auxiliary improves F1 at the configurations we measured; the default training therefore uses BCE alone. See Ablation Study for the evidence.

Repository Structure

CAFF/
|-- caff/                            # Core package (importable)
|   |-- __init__.py                  # Public API surface
|   |-- config.py                    # CAFFConfig + AblationFlags dataclasses
|   |-- csv.py                       # Contextual Summary Vector
|   |-- data.py                      # KG loader, BFS extractor, datasets
|   |-- dbm.py                       # Dynamic Bilinear Modulation
|   |-- encoders.py                  # Frozen encoder + relation cache
|   |-- evaluator.py                 # Metrics, MAP / NDCG, threshold tuning
|   |-- losses.py                    # BCE + DC + HC3 loss objects
|   |-- miners.py                    # DCMiner + HC3Miner + buffers
|   |-- model.py                     # CAFFModel (CSV + DBM + scoring head)
|   |-- scorer.py                    # DepthBilinear + HopScorer
|   |-- trainer.py                   # CAFFTrainer + CheckpointManager
|   `-- utils/                       # seeding, logging
|
|-- scripts/                         # Reproduction pipeline
|   |-- convert_orphanet_xml_to_tsv.py
|   |-- convert_hpo_to_tsv.py
|   |-- build_kg.py
|   |-- merge_hpo_into_kg.py
|   |-- build_orphanet_qa.py
|   |-- annotate_triples.py
|   |-- extract_bfs.py
|   |-- threshold_sweep.py
|   `-- per_hop_threshold_sweep.py
|
|-- configs/                         # YAML training configs (see Configurations below)
|   |-- no_dc.yaml                   # Default training configuration
|   |-- caff_orphanet.yaml           # Alternative with depth-contrastive loss
|   |-- caff_no_hc3.yaml             # Ablation (HC3 loss off)
|   |-- no_csv.yaml                  # Ablation (CSV off)
|   |-- no_dbm.yaml                  # Ablation (DBM off)
|   |-- no_freqcap.yaml              # Ablation (frequency cap off)
|   |-- depthbilinear.yaml           # Baseline (all CAFF components off)
|   `-- caff_smoke.yaml              # CI smoke test (tiny synthetic KG)
|
|-- tests/                           # Unit tests (run by CI)
|-- .github/workflows/tests.yml      # CI: lint + pytest on every push
|
|-- data/                            # gitignored (raw + processed)
|-- runs/                            # gitignored (checkpoints, logs)
|-- cache/                           # gitignored (BFS + relation cache)
|-- results/                         # benchmark JSON outputs
|
|-- train.py                         # Training entry point
|-- evaluate.py                      # Standalone evaluation script
|
|-- README.md                        # This file
|-- PAPER_DISCREPANCIES.md           # Detailed experimental log (26 sections)
|-- LICENSE                          # MIT
|-- requirements.txt
`-- .gitignore

PAPER_DISCREPANCIES.md is the running experimental log; every empirical claim in this README is backed by a numbered section there.

Installation

Prerequisites

Python >= 3.10
CUDA 11.8 (an 8 GB consumer GPU is sufficient)
Git

Setup

bash

# 1. Clone the repository
git clone https://github.com/<your-org>/caff.git
cd caff

# 2. Create a clean virtual environment
python -m venv .venv
source .venv/bin/activate          # Windows: .venv\Scripts\activate

# 3. Install PyTorch
pip install torch>=2.0 --index-url https://download.pytorch.org/whl/cu118

# 4. Install remaining dependencies
pip install -r requirements.txt

# 5. Pre-download the BioLinkBERT-Large encoder (optional, also auto-downloads)
python -c "from transformers import AutoModel; AutoModel.from_pretrained('michiyasunaga/BioLinkBERT-large')"

Core dependencies

torch>=2.0
transformers>=4.30
networkx>=3.0
numpy, scipy, scikit-learn, pandas
tqdm, pyyaml

Data

CAFF operates on a merged biomedical knowledge graph built from three public sources: Orphanet (rare-disease ontology, gene-disease links), HPO (phenotype ontology), and OMIM annotations (Mendelian inheritance, gene-phenotype). All three are publicly available and non-credentialed.

Build the KG

bash

# 1. Convert raw ontologies to TSV
python scripts/convert_orphanet_xml_to_tsv.py --in data/raw/orphanet/ --out data/processed/orphanet.tsv
python scripts/convert_hpo_to_tsv.py          --in data/raw/hpo/hp.obo  --out data/processed/hpo.tsv

# 2. Build base KG and merge in HPO/OMIM
python scripts/build_kg.py          --orphanet data/processed/orphanet.tsv --out data/processed/merged_kg.tsv
python scripts/merge_hpo_into_kg.py --in data/processed/merged_kg.tsv --hpo data/processed/hpo.tsv --out data/processed/merged_kg_v2.tsv

# 3. Sample QA records from the KG
python scripts/build_orphanet_qa.py --kg data/processed/merged_kg_v2.tsv --n 20000 --out data/processed/

# 4. Pre-compute BFS candidates and gold annotations
python scripts/extract_bfs.py       --kg data/processed/merged_kg_v2.tsv --L 3 --K_r 20
python scripts/annotate_triples.py  --kg data/processed/merged_kg_v2.tsv --qa data/processed/

KG statistics

Property	Value
Entities `	V
Triples `	E
Relation types `	R
Maximum BFS hop depth `L`	3
QA records (train/dev/test)	14,000 / 3,000 / 3,000
Triple instances (train)	473,471 (6.24% positive)
Triple instances (test)	102,317 (6.27% positive)

Triples on any shortest path from a seed entity to the gold answer entity receive label y=1; all others y=0. The test set is held out completely from training and threshold tuning.

Training

The default training configuration is configs/no_dc.yaml. To reproduce the headline numbers on three seeds:

bash

for s in 42 1337 2024; do
    python train.py --config configs/no_dc.yaml --seed $s
done

Each seed takes about 40 minutes on an 8 GB consumer GPU (RTX 4060). train.py auto-detects CUDA and applies hardware-appropriate overrides (micro_batch_size=4, grad_accum_steps=64, mixed_precision=fp16, effective batch 256). Training is deterministic: the same seed produces bit-identical results across runs on the same hardware.

Reproduce the full ablation suite

bash

# Train each variant on 3 seeds
for cfg in no_dc caff_orphanet no_csv no_dbm no_freqcap caff_no_hc3 depthbilinear; do
    for s in 42 1337 2024; do
        python train.py --config configs/$cfg.yaml --seed $s
    done
done

# Per-hop threshold tuning on each variant
for cfg in no_dc caff_orphanet no_csv no_dbm no_freqcap depthbilinear; do
    for s in 42 1337 2024; do
        python scripts/per_hop_threshold_sweep.py \
            --config configs/$cfg.yaml \
            --checkpoint runs/$cfg/seed_$s/best.pt
    done
done

Key hyperparameters (`no_dc.yaml`)

Hyperparameter	Value
Optimizer	AdamW (weight decay 1e-2)
Base learning rate	3e-4 (cosine to 1e-5, 1-epoch warmup)
Effective batch size	256
Epochs	10 (early stopping on dev F1, patience 5)
Gradient clip	1.0
Encoder	BioLinkBERT-large (340 M, frozen, d=1024)
Random seeds	{42, 1337, 2024}

Evaluation

Per-hop threshold sweep (the headline metric)

bash

python scripts/per_hop_threshold_sweep.py \
    --config configs/no_dc.yaml \
    --checkpoint runs/no_dc/seed_42/best.pt

Tunes per-hop retention thresholds on the dev set, then reports precision, recall, and F1 on the held-out test set under three regimes: global theta=0.50, global theta=0.80, and per-hop tuned thresholds.

Paired bootstrap significance test

bash

python evaluate.py \
    --checkpoint runs/no_dc/seed_42/best.pt \
    --report-bootstrap-vs runs/caff_orphanet/seed_42/best.pt \
    --mode autoregressive \
    --output-json results/bench_no_dc_vs_full_seed_42.json

Reports test metrics (F1, MAP, NDCG@10, per-hop precision) at theta=0.80 in autoregressive inference mode (no leakage of gold relations from prior hops), plus a paired bootstrap on per-query AP versus the baseline checkpoint (10,000 resamples).

Results

All numbers below are measured on the held-out Orphanet QA test set (3,000 queries, 102,317 candidate triples), 3 seeds, deterministic. The full per-seed outputs are in results/.

Default configuration (no_dc.yaml)

Metric	Mean +/- std	Inference mode
Test F1 (per-hop)	0.5764 +/- 0.0022	teacher-forced
Test F1 (autoregressive)	0.5477 +/- 0.0006	autoregressive
Test MAP	0.6741 +/- 0.0003	autoregressive
Test NDCG@10	0.7090 +/- 0.0003	autoregressive
Hop-1 precision	0.8234 +/- 0.0047	autoregressive
Hop-2 precision	0.4378 +/- 0.0006	autoregressive
Hop-3 precision	0.2426 +/- 0.0026	autoregressive

Per-hop is the headline metric: thresholds are tuned per hop on the dev set, then applied unchanged on test. Autoregressive is reported separately because it does not leak gold relations from prior hops during inference; the F1 gap of about 0.029 between the two modes is the cost of realistic deployment.

Statistical significance versus alternative configurations

Paired bootstrap on per-query AP, 10,000 resamples, computed per seed:

Comparison	delta_AP	95% CI	p-value
no_dc vs caff_orphanet, seed 42	+0.0295	[+0.0251, +0.0340]	0.0000
no_dc vs caff_orphanet, seed 1337	+0.0227	[+0.0188, +0.0267]	0.0000
no_dc vs caff_orphanet, seed 2024	+0.0227	[+0.0190, +0.0267]	0.0000
mean	+0.0250	(each CI excludes 0)	< 0.01

The default configuration outperforms the alternative (caff_orphanet.yaml, which adds the depth-contrastive auxiliary loss) significantly on every seed.

Generalization to novel seed entities

The Orphanet test set was not constructed to share seeds with training. Of the 2,876 distinct seed entities in the test set, 1,840 (64.2 percent of distinct seeds, and 64.2 percent of test queries) do not appear anywhere in training. Stratifying F1 by this seen / unseen distinction (3 seeds, theta=0.80, autoregressive):

group	n queries	F1 (mean +/- std)
seen seed	1,074	0.5642 +/- 0.0006
unseen seed	1,926	0.5384 +/- 0.0013
gap	--	+0.0259 +/- 0.0018 (4.6% relative)

Recall is nearly identical between the two groups; only precision drops on novel seeds. The 4.6 percent gap is concentrated at hop 2 (12.0 percent relative there); hops 1 and 3 show no measurable dependence on whether the seed was seen during training. Full analysis in PAPER_DISCREPANCIES.md Section 30. CAFF generalizes to novel seed entities within the same KG schema.

Per-relation breakdown

The Orphanet test set has 11 relation types, but the positives are concentrated in two: is_a (73.8 percent) and has_phenotype (24.1 percent). Splitting the headline F1 by relation reveals that CAFF performs very differently on the two:

relation	n_total	n_pos	precision	recall	F1 (mean +/- std)
`is_a`	75,469	5,214	0.534	0.687	0.6014 +/- 0.0013
`has_phenotype`	24,692	1,146	0.387	0.033	0.0604 +/- 0.0097
9 other (rare) relations	~2,156	56	varies	varies	~0 (data sparse)
overall	102,317	6,416	0.528	0.568	0.5477 +/- 0.0006

(Autoregressive mode, theta=0.80, 3 seeds.) The 0.5477 overall F1 is essentially the is_a F1 averaged with a near-zero has_phenotype contribution. The model handles taxonomy edges very well; it learns has_phenotype (recall recovers from 0.033 at theta=0.80 to 0.685 at theta=0.50 on the same checkpoint) but its confidence rankings on phenotype attachments are weaker. Per-relation thresholds do not raise the aggregate F1: has_phenotype caps at F1 = 0.20 even at its peak threshold (theta=0.65), and is_a already dominates the average. Full analysis in PAPER_DISCREPANCIES.md Section 27.

Ablation Study

Leave-one-out over every component, plus a depth-stratified baseline, on the held-out test set. Three seeds per variant; per-hop test F1 with thresholds tuned on dev.

Variant	Test F1 (per-hop)	delta vs Default
Default (no_dc.yaml)	0.5764 +/- 0.0022	--
no_dc + HC3 (`caff_no_hc3` off)	0.5524 +/- 0.0016	-0.0240
no_dc + DC (`caff_orphanet.yaml`)	0.5524 +/- 0.0016	-0.0240
no_dc - FreqCap	0.5524 (identical to caff_orphanet, frequency cap inert on this KG)	-0.0240
no_dc - DBM	0.5063 +/- 0.0046	-0.0701
no_dc - CSV	0.5054 +/- 0.0027	-0.0710
DepthBilinear (no CSV, no DBM)	0.4966 +/- 0.0121	-0.0798

Take-aways:

CSV and DBM are the essential architectural components. Removing either drops test F1 by about 0.07 points; they form a coupled pair (CSV produces z, DBM consumes it), so removing one effectively breaks the context-aware path.
The depth-contrastive auxiliary loss hurts at lambda_D=0.40. Adding it back (i.e., switching from no_dc to caff_orphanet) costs 0.024 F1 (paired bootstrap p < 0.01 across three seeds). A smaller positive lambda_D is left to future work; the default disables DC.
HC3 is inert. The HC3 loss as implemented produces zero gradient at the configurations tested (positives and negatives collide at the teacher-forced training step); turning it on changes neither the gradients nor held-out F1. An attempted cross-query variant raised the loss gradient norm but did not change test F1. Detailed diagnostics are in PAPER_DISCREPANCIES.md Sections 22-23.
The per-relation frequency cap is inert here. The KG has only 11 relations after min_relation_freq=50 at load time, so the cap has nothing to act on.

The full evidence trail, including code-level verification that no_dc.yaml differs from caff_orphanet.yaml only in the DC loss weight, is in PAPER_DISCREPANCIES.md Sections 22-26.

Configurations

Config file	Purpose	Trained?	Test F1 (per-hop)
`no_dc.yaml`	Default training configuration	Yes	0.5764 +/- 0.0022
`caff_orphanet.yaml`	Alternative with DC loss on	Yes	0.5524 +/- 0.0016
`caff_no_hc3.yaml`	Ablation (HC3 off, DC on)	Yes	0.5524 (HC3 inert)
`no_csv.yaml`	Ablation (CSV off)	Yes	0.5054 +/- 0.0027
`no_dbm.yaml`	Ablation (DBM off)	Yes	0.5063 +/- 0.0046
`no_freqcap.yaml`	Ablation (freq cap off)	Yes	0.5524 (cap inert)
`depthbilinear.yaml`	Baseline (all CAFF components off)	Yes	0.4966 +/- 0.0121
`caff_smoke.yaml`	CI smoke test (tiny synthetic KG)	Yes (CI)	n/a
`caff_full.yaml`	Legacy paper-spec config, kept for reference; not runnable as-is (`d=768` does not match BioLinkBERT-Large's output of 1024)	No	n/a

All trained variants have checkpoints under runs/<config_name>/seed_<seed>/.

Hyperparameters

The default configuration (no_dc.yaml) uses:

Symbol	Meaning	Value
`d`	Embedding dimension (BioLinkBERT-Large output)	1024
`L`	Maximum BFS hop depth	3
`rho`	DBM rank	16
`theta`	Retention threshold (global default)	0.80
`K_r`	Frequency cap per relation per head	20
`lambda_C`	HC3 loss weight	0.35 (inert)
`lambda_D`	Depth-contrastive loss weight	0.0 (default; 0.40 disabled)
`min_relation_freq`	Drop singleton relations at KG load	50
`gamma_C`	HC3 margin	0.25
`gamma_D`	Depth-contrastive margin	0.20

A theta sensitivity analysis and a lambda_D sweep are listed under future work.

Reproducibility

All results are mean across three seeds {42, 1337, 2024}.
Training is deterministic (config.deterministic = true).
Standard deviations are reported in every results table.
Per-seed benchmark JSON outputs are committed under results/.
Checkpoints under runs/<config_name>/seed_<seed>/best.pt.
A full mirror of trained checkpoints, runs, and cache is maintained on Hugging Face at https://huggingface.co/MrDhifallah/CAFF.

Hardware

Stage	Reference setup	Time per seed
KG build + BFS	i9-13900H, 32 GB RAM	~5 min one-time
Training	NVIDIA RTX 4060 Laptop, 8 GB	~40 min
Per-hop threshold sweep	RTX 4060	~10 min
Paired bootstrap eval	RTX 4060	~3 min

Pretty much any modern 8 GB consumer GPU suffices. CPU-only training is technically supported (via train.py's automatic hardware override), but is impractical because the BioLinkBERT-Large encoder consumes about 9 GB of CPU RAM and runs roughly 8x slower than on the GPU.

Scope and Future Work

This release evaluates CAFF on a single, well-characterized biomedical benchmark. The following are sensible next steps; none of them are implemented in this release.

Lambda_D sweep. The default disables the depth-contrastive auxiliary because it hurts at lambda_D=0.40. Whether a smaller positive value (0.05 to 0.20) helps is open.
K-fold cross-validation. The current results use a fixed 14K/3K/3K split. A 5-fold cross-validation would tighten the variance estimates.
Theta sensitivity analysis. The headline uses theta=0.80 from a dev sweep; reporting F1 across theta in [0.5, 0.9] would document the operating-point behavior more thoroughly.
Typed CSV for semantic relations. The per-relation breakdown above (and Section 27 of PAPER_DISCREPANCIES.md) shows that CAFF reaches F1 = 0.60 on is_a but caps at F1 = 0.20 on has_phenotype, even when the threshold is tuned per relation. The mean-pool CSV compresses ontological chains cleanly but discards information that matters for many-to-many semantic relations. A typed CSV that keeps head and tail entity types from the retained set is the natural next step.
External rare-disease benchmark. Datasets such as RareBench would test CAFF's transfer behavior on data not sampled from the training KG.
End-to-end question answering with an LLM backbone. This release measures the filtering layer only (F1, MAP, NDCG, per-hop precision). Connecting CAFF's filtered output to an LLM and measuring downstream QA accuracy is a separate engineering task.
Larger KGs and broader biomedical domains. Datasets like DisGeNET or UMLS require institutional access and are not used here. Validating CAFF on a broader KG is future work.
A typed CSV. The CSV currently pools relation embeddings; head/tail entity types in the retained set are discarded. A typed variant could carry additional signal.

Citation

bibtex

@article{dhifallah2026caff,
  title   = {{CAFF}: Context-Aware Feedback Filtering for Multi-Hop
             Biomedical Knowledge Graph Evidence Selection},
  author  = {Dhifallah, Marwan and Liu, Yu},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  year    = {2026},
  note    = {Under review}
}

The empirical results in this README are documented in PAPER_DISCREPANCIES.md (Sections 22-26 for the ablation, paired bootstrap, and configuration analysis).

License

This project is released under the MIT License; see LICENSE for the full text.

The merged KG derived from Orphanet, HPO, and OMIM is not redistributed; users must obtain the source data directly under each provider's terms.

Acknowledgements

This research was conducted at the School of Software Engineering, Dalian University of Technology (DUT), with support from the CSC Type-B Scholarship. We thank the maintainers of Orphanet, HPO, OMIM, and BioLinkBERT for making their resources publicly available.

Contact

Role	Name	Email
Corresponding author	Marwan Dhifallah (M.Sc. student, DUT)	marwan@mail.dlut.edu.cn
Supervisor	Prof. Yu Liu (Full Professor, DUT)	yuliu@dlut.edu.cn

For bugs and feature requests, please open an issue. For research collaborations, please contact the corresponding author directly.

MrDhifallah/CAFF

CAFF: Context-Aware Feedback Filtering for Multi-Hop Biomedical Knowledge Graph Evidence Selection

Table of Contents

TL;DR

The Context Blindness Error

Approach

Stage 1 -- BFS candidate stratification

Stage 2 -- Contextual Summary Vector (CSV)

Stage 3 -- Dynamic Bilinear Modulation (DBM)

Stage 4 -- Training objective

Repository Structure

Installation

Prerequisites

Setup

Core dependencies

Data

Build the KG

KG statistics

Training

Reproduce the full ablation suite

Key hyperparameters (no_dc.yaml)

Evaluation

Per-hop threshold sweep (the headline metric)

Paired bootstrap significance test

Results

Default configuration (no_dc.yaml)

Statistical significance versus alternative configurations

Generalization to novel seed entities

Per-relation breakdown

Ablation Study

Configurations

Hyperparameters

Reproducibility

Hardware

Scope and Future Work

Citation

License

Acknowledgements

Contact

MrDhifallah/
CAFF

Key hyperparameters (`no_dc.yaml`)