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20260710_firstorder_synccanary__20260710_firstorder_synccanary-interaction_coefficient_group_l1_penalty0
fnbm-current
20260710_firstorder_synccanary
20260710_firstorder_synccanary-interaction_coefficient_group_l1_penalty0
{"model.kwargs.interaction_coefficient_group_l1_penalty": 0}
13268200
torch
2026-07-10T15:43:20.942004+00:00
data: batch_size: 128 downstream_sequence: '' num_workers: 2 pin_memory: true sequence_column: sequence target_column: label test_csv: /scratch/abr10036/projects/2025-interactions/datasets/202607_simulations/20260707_first_order/test.csv train_csv: /scratch/abr10036/projects/2025-interactions/datasets/2...
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[{"epoch": 0, "train_loss": 0.9912525415420532, "val_loss": 0.9611283802682427, "val_pearson_r": 0.20023013651371002, "lr": 0.0005, "epoch_time_s": 35.89, "grad_norm_mean": 0.7054833173751831, "grad_norm_max": 3.9280409812927246, "reg_conv_kernel_l1_raw": 44.472137451171875, "reg_conv_kernel_l1_scaled": 0.0, "reg_conv_...
https://wandb.ai/arushml/FactorizedNBM/runs/ao9u2t1t
/scratch/abr10036/projects/2025-interactions/results/20260710_firstorder_synccanary/20260710_firstorder_synccanary-interaction_coefficient_group_l1_penalty0/interpretation/epoch_14/simulation_identifiability/report.pdf
20260710_firstorder_synccanary__20260710_firstorder_synccanary-interaction_coefficient_group_l1_penalty0.0001
fnbm-current
20260710_firstorder_synccanary
20260710_firstorder_synccanary-interaction_coefficient_group_l1_penalty0.0001
{"model.kwargs.interaction_coefficient_group_l1_penalty": 0.0001}
13268200
torch
2026-07-10T15:52:55.077214+00:00
data: batch_size: 128 downstream_sequence: '' num_workers: 2 pin_memory: true sequence_column: sequence target_column: label test_csv: /scratch/abr10036/projects/2025-interactions/datasets/202607_simulations/20260707_first_order/test.csv train_csv: /scratch/abr10036/projects/2025-interactions/datasets/2...
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[{"epoch": 0, "train_loss": 0.9912760257720947, "val_loss": 0.9611749516171255, "val_pearson_r": 0.20003849267959595, "lr": 0.0005, "epoch_time_s": 50.66, "grad_norm_mean": 0.7055649161338806, "grad_norm_max": 3.9280409812927246, "reg_conv_kernel_l1_raw": 44.45408630371094, "reg_conv_kernel_l1_scaled": 0.0, "reg_conv_k...
https://wandb.ai/arushml/FactorizedNBM/runs/71uoykh0
/scratch/abr10036/projects/2025-interactions/results/20260710_firstorder_synccanary/20260710_firstorder_synccanary-interaction_coefficient_group_l1_penalty0.0001/interpretation/epoch_14/simulation_identifiability/report.pdf
20260710_firstorder_synccanary__20260710_firstorder_synccanary-interaction_coefficient_group_l1_penalty1e-5
fnbm-current
20260710_firstorder_synccanary
20260710_firstorder_synccanary-interaction_coefficient_group_l1_penalty1e-5
{"model.kwargs.interaction_coefficient_group_l1_penalty": 1e-05}
13268200
torch
2026-07-10T15:48:06.033082+00:00
data: batch_size: 128 downstream_sequence: '' num_workers: 2 pin_memory: true sequence_column: sequence target_column: label test_csv: /scratch/abr10036/projects/2025-interactions/datasets/202607_simulations/20260707_first_order/test.csv train_csv: /scratch/abr10036/projects/2025-interactions/datasets/2...
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[{"epoch": 0, "train_loss": 0.9912598133087158, "val_loss": 0.9611444594753775, "val_pearson_r": 0.2001277655363083, "lr": 0.0005, "epoch_time_s": 50.32, "grad_norm_mean": 0.7055728435516357, "grad_norm_max": 3.9280409812927246, "reg_conv_kernel_l1_raw": 44.453514099121094, "reg_conv_kernel_l1_scaled": 0.0, "reg_conv_k...
https://wandb.ai/arushml/FactorizedNBM/runs/018k7qw6
/scratch/abr10036/projects/2025-interactions/results/20260710_firstorder_synccanary/20260710_firstorder_synccanary-interaction_coefficient_group_l1_penalty1e-5/interpretation/epoch_14/simulation_identifiability/report.pdf
"20260710_firstorder_finegrid__20260710_firstorder_finegrid-seed0-interaction_coefficient_group_l1_p(...TRUNCATED)
fnbm-current
20260710_firstorder_finegrid
"20260710_firstorder_finegrid-seed0-interaction_coefficient_group_l1_penalty0.001-order2_contributio(...TRUNCATED)
"{\"run.seed\": 0, \"model.kwargs.interaction_coefficient_group_l1_penalty\": 0.001, \"model.kwargs.(...TRUNCATED)
13305710
torch
2026-07-11T03:17:25.296663+00:00
"data:\n batch_size: 128\n downstream_sequence: ''\n num_workers: 2\n pin_memory: true\n sequen(...TRUNCATED)
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"[{\"epoch\": 0, \"train_loss\": 0.9663487076759338, \"val_loss\": 0.8846952501375964, \"val_pearson(...TRUNCATED)
https://wandb.ai/arushml/FactorizedNBM/runs/t5yqygrs
"/scratch/abr10036/projects/2025-interactions/results/20260710_firstorder_finegrid/20260710_firstord(...TRUNCATED)
"20260710_firstorder_finegrid__20260710_firstorder_finegrid-seed0-interaction_coefficient_group_l1_p(...TRUNCATED)
fnbm-current
20260710_firstorder_finegrid
"20260710_firstorder_finegrid-seed0-interaction_coefficient_group_l1_penalty0.001-order2_contributio(...TRUNCATED)
"{\"run.seed\": 0, \"model.kwargs.interaction_coefficient_group_l1_penalty\": 0.001, \"model.kwargs.(...TRUNCATED)
13305710
torch
2026-07-11T03:19:11.571067+00:00
"data:\n batch_size: 128\n downstream_sequence: ''\n num_workers: 2\n pin_memory: true\n sequen(...TRUNCATED)
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"[{\"epoch\": 0, \"train_loss\": 0.9787513613700867, \"val_loss\": 0.8996884800066614, \"val_pearson(...TRUNCATED)
https://wandb.ai/arushml/FactorizedNBM/runs/qpy5m6b3
"/scratch/abr10036/projects/2025-interactions/results/20260710_firstorder_finegrid/20260710_firstord(...TRUNCATED)
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fnbm-current
20260710_firstorder_finegrid
"20260710_firstorder_finegrid-seed0-interaction_coefficient_group_l1_penalty0.001-order2_contributio(...TRUNCATED)
"{\"run.seed\": 0, \"model.kwargs.interaction_coefficient_group_l1_penalty\": 0.001, \"model.kwargs.(...TRUNCATED)
13305710
torch
2026-07-11T03:22:04.805002+00:00
"data:\n batch_size: 128\n downstream_sequence: ''\n num_workers: 2\n pin_memory: true\n sequen(...TRUNCATED)
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"[{\"epoch\": 0, \"train_loss\": 0.9665890336036682, \"val_loss\": 0.8780847317094256, \"val_pearson(...TRUNCATED)
https://wandb.ai/arushml/FactorizedNBM/runs/tv3bjzel
"/scratch/abr10036/projects/2025-interactions/results/20260710_firstorder_finegrid/20260710_firstord(...TRUNCATED)
"20260710_firstorder_finegrid__20260710_firstorder_finegrid-seed0-interaction_coefficient_group_l1_p(...TRUNCATED)
fnbm-current
20260710_firstorder_finegrid
"20260710_firstorder_finegrid-seed0-interaction_coefficient_group_l1_penalty0.001-order2_contributio(...TRUNCATED)
"{\"run.seed\": 0, \"model.kwargs.interaction_coefficient_group_l1_penalty\": 0.001, \"model.kwargs.(...TRUNCATED)
13305710
torch
2026-07-11T03:22:13.883213+00:00
"data:\n batch_size: 128\n downstream_sequence: ''\n num_workers: 2\n pin_memory: true\n sequen(...TRUNCATED)
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"[{\"epoch\": 0, \"train_loss\": 0.9788815379142761, \"val_loss\": 0.9016267471252732, \"val_pearson(...TRUNCATED)
https://wandb.ai/arushml/FactorizedNBM/runs/yq0c987j
"/scratch/abr10036/projects/2025-interactions/results/20260710_firstorder_finegrid/20260710_firstord(...TRUNCATED)
"20260710_firstorder_finegrid__20260710_firstorder_finegrid-seed0-interaction_coefficient_group_l1_p(...TRUNCATED)
fnbm-current
20260710_firstorder_finegrid
"20260710_firstorder_finegrid-seed0-interaction_coefficient_group_l1_penalty0.0005-order2_contributi(...TRUNCATED)
"{\"run.seed\": 0, \"model.kwargs.interaction_coefficient_group_l1_penalty\": 0.0005, \"model.kwargs(...TRUNCATED)
13305710
torch
2026-07-11T04:02:58.550607+00:00
"data:\n batch_size: 128\n downstream_sequence: ''\n num_workers: 2\n pin_memory: true\n sequen(...TRUNCATED)
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"[{\"epoch\": 0, \"train_loss\": 0.9664555191993713, \"val_loss\": 0.8872096257604611, \"val_pearson(...TRUNCATED)
https://wandb.ai/arushml/FactorizedNBM/runs/hdikjne6
"/scratch/abr10036/projects/2025-interactions/results/20260710_firstorder_finegrid/20260710_firstord(...TRUNCATED)
"20260710_firstorder_finegrid__20260710_firstorder_finegrid-seed0-interaction_coefficient_group_l1_p(...TRUNCATED)
fnbm-current
20260710_firstorder_finegrid
"20260710_firstorder_finegrid-seed0-interaction_coefficient_group_l1_penalty0.0005-order2_contributi(...TRUNCATED)
"{\"run.seed\": 0, \"model.kwargs.interaction_coefficient_group_l1_penalty\": 0.0005, \"model.kwargs(...TRUNCATED)
13305710
torch
2026-07-11T04:05:11.674544+00:00
"data:\n batch_size: 128\n downstream_sequence: ''\n num_workers: 2\n pin_memory: true\n sequen(...TRUNCATED)
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https://wandb.ai/arushml/FactorizedNBM/runs/2u28udok
"/scratch/abr10036/projects/2025-interactions/results/20260710_firstorder_finegrid/20260710_firstord(...TRUNCATED)
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fnbm-current
20260710_firstorder_finegrid
"20260710_firstorder_finegrid-seed0-interaction_coefficient_group_l1_penalty0.0005-order2_contributi(...TRUNCATED)
"{\"run.seed\": 0, \"model.kwargs.interaction_coefficient_group_l1_penalty\": 0.0005, \"model.kwargs(...TRUNCATED)
13305710
torch
2026-07-11T04:07:18.775671+00:00
"data:\n batch_size: 128\n downstream_sequence: ''\n num_workers: 2\n pin_memory: true\n sequen(...TRUNCATED)
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https://wandb.ai/arushml/FactorizedNBM/runs/smbhqb2l
"/scratch/abr10036/projects/2025-interactions/results/20260710_firstorder_finegrid/20260710_firstord(...TRUNCATED)
End of preview. Expand in Data Studio

fnbm-current-sweep-results

Grid-sweep trial results for fnbm-current.

Dataset Info

  • Rows: 23
  • Columns: 29

Columns

Column Type Description
trial_id Value('string') Unique id for this trial: {sweep_name}__{combo_name}
experiment_name Value('string') Experiment folder slug this trial belongs to, e.g. fnbm-current
sweep_name Value('string') Base run name for the whole sweep (config['run']['name'] before per-combo suffixing)
combo_name Value('string') Per-combo run name (base name + swept param values)
grid_overrides Value('string') JSON-encoded {dot.path: value} of just the swept parameters for this trial
job_id Value('string') Cluster job id, if known
cluster Value('string') Cluster name, if known
timestamp Value('string') ISO 8601 UTC time this record was built
config_yaml Value('string') Full resolved config for this trial (reproducibility source of truth)
best_val_loss Value('float64') Best validation loss achieved during training
best_val_pearson_r Value('float64') Validation Pearson r at the best-val-loss epoch
best_epoch Value('int64') Epoch index of the best validation loss
sim_r2 Value('float64') Final-epoch R^2 against simulation ground truth
sim_pearson_r Value('float64') Final-epoch Pearson r against simulation ground truth
sim_mean_gt_z_abs_ratio Value('float64') Ground-truth mean fraction of
sim_mean_model_z_abs_ratio Value('float64') Model's mean fraction of
sim_z_abs_1_corr Value('float64') Correlation between model and ground-truth per-sample first-order
sim_z_abs_2_corr Value('float64') Correlation between model and ground-truth per-sample second-order
sim_z_abs_ratio_corr Value('float64') Correlation between model and ground-truth per-sample z_abs_ratio
sim_z_abs_ratio_mse Value('float64') MSE between model and ground-truth per-sample z_abs_ratio
absorption_gap Value('float64') sim_mean_model_z_abs_ratio - sim_mean_gt_z_abs_ratio (signed; >0 = over-attributes to second order)
promiscuity_mean_entropy Value('float64') Mean per-filter normalized positional entropy (diffuse activation proxy)
promiscuity_max_entropy Value('float64') Max per-filter normalized positional entropy
n_true_interaction_pairs Value('float64') Ground truth: number of true interacting motif pairs in the simulation (from sim_config.json)
interaction_effective_num_pairs Value('float64') Effective number of filter pairs carrying interaction mass (entropy-based)
interaction_concentration_gap Value('float64') interaction_effective_num_pairs - n_true_interaction_pairs
epoch_history Value('string') JSON-encoded list of per-epoch metrics.csv rows for this trial
wandb_run_url Value('string') Live wandb run URL for this trial, if wandb was enabled
report_pdf_path Value('string') Cluster-local path to this trial's latest simulation_eval report.pdf, if any

Generation Parameters

{
  "script_name": "sync_sweep_results.py",
  "model": "fnbm-current",
  "description": "Grid-sweep trial results for fnbm-current.",
  "experiment_name": "fnbm-current",
  "experiment_id": "fnbm-current",
  "visualizer_type": "table",
  "artifact_type": "eval_result",
  "hyperparameters": {},
  "input_datasets": []
}

Usage

from datasets import load_dataset

dataset = load_dataset("arushram/fnbm-current-sweep-results", split="train")
print(f"Loaded {len(dataset)} rows")

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